Show of Hands:Votes: Party classification:: Q109244No_AllXpreProc_cnk02_rest
bdanalytics
Date: (Sun) Jun 12, 2016
Introduction:
Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv”
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv”
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Add n-grams for glbFeatsText
- fit.models chunk:
- Classification: Plot AUC Curves for all models & highlight glbMdlSel
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
if (is.null(knitr::opts_current$get(name = 'label'))) # Running in IDE
debugSource("~/Dropbox/datascience/R/mydsutils.R") else
source("~/Dropbox/datascience/R/mydsutils.R") ## Loading required package: caret## Loading required package: lattice# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 10 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(knitr)## Loading required package: knitrrequire(stringr)## Loading required package: stringr#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>";
# or named collection of <PathPointer>s
# sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
# or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
#, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
# select from c("copy", NULL ???, "condition", "sample", )
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
# ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'
# )
)
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv")
glbObsDropCondition <- #NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
# 'is.na(glbObsAll[, glb_rsp_var_raw])'
# '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
# 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
'(is.na(glbObsAll[, "Q109244"]) | (glbObsAll[, "Q109244"] != "No"))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "Party"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL
function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
#
# chk ref value against frequencies vs. alpha sort order
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "D"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
}
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))
glb_map_rsp_var_to_raw <- #NULL
function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# levels(var)[as.numeric(var)]
sapply(levels(var)[as.numeric(var)], function(elm)
if (is.na(elm)) return(elm) else
if (elm == 'R') return("Republican") else
if (elm == 'D') return("Democrat") else
stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
)
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>")
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>") -> OOB performed worse than "Hhold.fctr"
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category &
# work each one in
, "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel"
,"Q124742","Q124122"
,"Q123621","Q123464"
,"Q122771","Q122770","Q122769","Q122120"
,"Q121700","Q121699","Q121011"
,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012"
,"Q119851","Q119650","Q119334"
,"Q118892","Q118237","Q118233","Q118232","Q118117"
,"Q117193","Q117186"
,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
,"Q114961","Q114748","Q114517","Q114386","Q114152"
,"Q113992","Q113583","Q113584","Q113181"
,"Q112478","Q112512","Q112270"
,"Q111848","Q111580","Q111220"
,"Q110740"
,"Q109367","Q109244"
,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
,"Q107869","Q107491"
,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
,"Q105840","Q105655"
,"Q104996"
,"Q103293"
,"Q102906","Q102674","Q102687","Q102289","Q102089"
,"Q101162","Q101163","Q101596"
,"Q100689","Q100680","Q100562","Q100010"
,"Q99982"
,"Q99716"
,"Q99581"
,"Q99480"
,"Q98869"
,"Q98578"
,"Q98197"
,"Q98059","Q98078"
,"Q96024" # Done
,".pos")
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsInteractionOnly[["YOB.Age.dff"]] <- "YOB.Age.fctr"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
# mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) }
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(Name) { return(sapply(Name, function(thsName)
# str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(raw1) { return(1:length(raw1)) }
, args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
# mapfn = function(raw1) { return(1:length(raw1)) }
# , args = c(".rnorm"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
# mapfn = function(District) {
# raw <- District;
# ret_vals <- rep_len("NA", length(raw));
# ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm)
# ifelse(elm < 10, "1-9",
# ifelse(elm < 20, "10-19", "20+")));
# return(relevel(as.factor(ret_vals), ref = "NA"))
# }
# , args = c("District"))
# YOB options:
# 1. Missing data:
# 1.1 0 -> Does not improve baseline
# 1.2 Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
# raw[!is.na(raw) & raw >= 2010] <- NA
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
retVal <- rep_len("NA", length(raw))
# breaks = c(1879, seq(1949, 1989, 10), 2049)
# cutVal <- cut(raw[!is.na(raw)], breaks = breaks,
# labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
return(factor(retVal, levels = c("NA"
,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
ordered = TRUE))
}
, args = c("YOB"))
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.dff"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
# retVal <- rep_len(0, length(raw))
stopifnot(sum(!is.na(raw) && (raw <= 15)) == 0)
stopifnot(sum(!is.na(raw) && (raw >= 90)) == 0)
# msk <- !is.na(raw) && (raw > 15) && (raw <= 20); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 15
# msk <- !is.na(raw) && (raw > 20) && (raw <= 25); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 20
# msk <- !is.na(raw) && (raw > 25) && (raw <= 30); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 25
# msk <- !is.na(raw) && (raw > 30) && (raw <= 35); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 30
# msk <- !is.na(raw) && (raw > 35) && (raw <= 40); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 35
# msk <- !is.na(raw) && (raw > 40) && (raw <= 50); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 40
# msk <- !is.na(raw) && (raw > 50) && (raw <= 65); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 50
# msk <- !is.na(raw) && (raw > 65) && (raw <= 90); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 65
breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)
retVal <- sapply(raw, function(age) {
if (is.na(age)) return(0) else
if ((age > 15) && (age <= 20)) return(age - 15) else
if ((age > 20) && (age <= 25)) return(age - 20) else
if ((age > 25) && (age <= 30)) return(age - 25) else
if ((age > 30) && (age <= 35)) return(age - 30) else
if ((age > 35) && (age <= 40)) return(age - 35) else
if ((age > 40) && (age <= 50)) return(age - 40) else
if ((age > 50) && (age <= 65)) return(age - 50) else
if ((age > 65) && (age <= 90)) return(age - 65)
})
return(retVal)
}
, args = c("YOB"))
glbFeatsDerive[["Gender.fctr"]] <- list(
mapfn = function(raw1) {
raw <- raw1
raw[raw %in% ""] <- "N"
raw <- gsub("Male" , "M", raw, fixed = TRUE)
raw <- gsub("Female", "F", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("Gender"))
glbFeatsDerive[["Income.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("under $25,000" , "<25K" , raw, fixed = TRUE)
raw <- gsub("$25,001 - $50,000" , "25-50K" , raw, fixed = TRUE)
raw <- gsub("$50,000 - $74,999" , "50-75K" , raw, fixed = TRUE)
raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)
raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
raw <- gsub("over $150,000" , ">150K" , raw, fixed = TRUE)
return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
ordered = TRUE))
}
, args = c("Income"))
glbFeatsDerive[["Hhold.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)
raw <- gsub("Married (no kids)" , "MKn", raw, fixed = TRUE)
raw <- gsub("Married (w/kids)" , "MKy", raw, fixed = TRUE)
raw <- gsub("Single (no kids)" , "SKn", raw, fixed = TRUE)
raw <- gsub("Single (w/kids)" , "SKy", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("HouseholdStatus"))
glbFeatsDerive[["Edn.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Current K-12" , "K12", raw, fixed = TRUE)
raw <- gsub("High School Diploma" , "HSD", raw, fixed = TRUE)
raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
raw <- gsub("Associate's Degree" , "Ast", raw, fixed = TRUE)
raw <- gsub("Bachelor's Degree" , "Bcr", raw, fixed = TRUE)
raw <- gsub("Master's Degree" , "Msr", raw, fixed = TRUE)
raw <- gsub("Doctoral Degree" , "PhD", raw, fixed = TRUE)
return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
ordered = TRUE))
}
, args = c("EducationLevel"))
# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))
glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
mapfn = function(raw1) {
raw1[raw1 %in% ""] <- "NA"
rawVal <- unique(raw1)
if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
raw1 <- gsub("Idealist" , "Id", raw1, fixed = TRUE)
raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
}
return(relevel(as.factor(raw1), ref = "NA"))
}
, args = c(qsn))
# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
# mapfn = function(FertilityRate, Region) {
# RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
#
# retVal <- FertilityRate
# retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
# return(retVal)
# }
# , args = c("FertilityRate", "Region"))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE,
# last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : glbFeatsCluster <- c("YOB.Age.fctr", "Gender.fctr", "Income.fctr",
# # "Hhold.fctr",
# "Edn.fctr",
# paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".")) # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[",
# toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
# "]\\.[PT]\\."),
# names(glbObsAll), value = TRUE)
glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
# glbRFESizes[["RFE.X"]] <- c(96, 112, 120, 124, 128, 129, 130, 131, 132, 133, 135, 138, 142, 157, 187, 247) # accuracy(131) = 0.6285
# glbRFESizes[["Final"]] <- c(8, 16, 32, 40, 44, 46, 48, 49, 50, 51, 52, 56, 64, 96, 128, 247) # accuracy(49) = 0.6164
glbRFEResults <- NULL
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# max(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
c(NULL
))
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Add xgboost algorithm
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
,"xgbLinear","xgbTree"
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
,"xgbLinear","xgbTree"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart","xgbLinear","xgbTree"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "Csm.X", "All.X", "Best.Interact") %*% c(NUll, ".NOr", ".Inc")
# RFE = "Recursive Feature Elimination"
# Csm = CuStoM
# NOr = No OutlieRs
# Inc = INteraCt
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial) {
# glm does not work for multinomial
glbMdlFamilies[["All.X"]] <- c("glmnet")
} else {
# glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
glbMdlFamilies[["All.X"]] <- c("glmnet")
# glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm")
# glbMdlFamilies[["RFE.X"]] <- setdiff(glbMdlMethods, c(NULL
# , "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
# , "lda","lda2" # error: Error in lda.default(x, grouping, ...) : variable 236 appears to be constant within groups
# , "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
# , "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
# , "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !
# , "svmRadial" # didn't bother
# ,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
# ))
}
# glbMdlFamilies[["All.X.Inc"]] <- glbMdlFamilies[["All.X"]] # value not used
# glbMdlFamilies[["RFE.X.Inc"]] <- glbMdlFamilies[["RFE.X"]] # value not used
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(glbRFEResults)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
# glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")
glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE
glbMdlAllowParallel[["All.X#ica#rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X#zv.pca#rcv#glmnet"]] <- FALSE
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
AllX__rcv_glmnetTuneParams <- rbind(data.frame()
,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(parameter = "lambda", vals = "0.0356818417 0.05 0.06367626 0.07 0.09167068")
)
AllX_expoTransspatialSign_rcv_glmnetTuneParams <- rbind(data.frame()
,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(parameter = "lambda", vals = "0.0072065998 0.02 0.0334500732 0.04 0.05969355")
) # max.Accuracy.OOB = 0.5956175 @ 0.325 0.03345007
FinalAllX__rcv_glmnetTuneParams <- rbind(data.frame()
,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(parameter = "lambda", vals = "6.451187e-03 0.02 2.994376e-02 0.04 0.05343633")
)
FinalAllX_expoTransspatialSign_rcv_glmnetTuneParams <- rbind(data.frame()
,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(parameter = "lambda", vals = "6.487621e-03 0.02 3.011287e-02 0.04 0.05373812")
) # max.Accuracy.fit = 0.5991618 @ 0.55 0.03011287
glbMdlTuneParams <- rbind(glbMdlTuneParams,
cbind(data.frame(mdlId = "All.X##rcv#glmnet"), AllX__rcv_glmnetTuneParams),
cbind(data.frame(mdlId = "All.X#expoTrans.spatialSign#rcv#glmnet"),
AllX_expoTransspatialSign_rcv_glmnetTuneParams),
cbind(data.frame(mdlId = "Final.All.X##rcv#glmnet"), FinalAllX__rcv_glmnetTuneParams),
cbind(data.frame(mdlId = "Final.All.X#expoTrans.spatialSign#rcv#glmnet"),
FinalAllX_expoTransspatialSign_rcv_glmnetTuneParams))
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
bagEarthTuneParams <- rbind(data.frame()
,data.frame(parameter = "degree", vals = "1")
,data.frame(parameter = "nprune", vals = "256")
)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "Final.RFE.X.Inc##rcv#bagEarth"),
# bagEarthTuneParams))
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
pkgPreprocMethods <-
# caret version: 6.0.068 # packageVersion("caret")
# operations are applied in this order: zero-variance filter, near-zero variance filter, Box-Cox/Yeo-Johnson/exponential transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign
# *Impute methods needed only if NAs are fed to myfit_mdl
# Also, ordered.factor in caret creates features as Edn.fctr^4 which is treated as an exponent by bagImpute
c(NULL
,"zv", "nzv"
,"BoxCox", "YeoJohnson", "expoTrans"
,"center", "scale", "center.scale", "range"
,"knnImpute", "bagImpute", "medianImpute"
,"zv.pca", "ica", "spatialSign"
,"conditionalX")
glbMdlPreprocMethods <- list(# NULL # : default
"All.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
c("knnImpute", "bagImpute", "medianImpute")),
# c(NULL)))
c("expoTrans.spatialSign")))
)
# glbMdlPreprocMethods[["RFE.X"]] <- list("glmnet" = union(unlist(glbMdlPreprocMethods[["All.X"]]),
# "nzv.pca.spatialSign"))
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "min.elapsedtime.everything",
"max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB",
"min.elapsedtime.everything",
# "min.aic.fit",
"max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB", "min.elapsedtime.everything")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glbMdlEnsemble <- NULL #"auto"
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glbMdlSelId <- NULL #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)
glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. above
)
# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
# require(tidyr)
# obsOutFinDf <- obsOutFinDf %>%
# tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"),
# sep = "#", remove = TRUE, extra = "merge")
# # mnm prefix stands for max_n_mean
# mnmout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# #dplyr::top_n(1, Probability1) %>% # Score = 3.9426
# #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;
# #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169;
# dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;
# #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#
# # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
# dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)),
# yMeanN = weighted.mean(as.numeric(y), c(Probability1)))
#
# maxout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# dplyr::summarize(maxProb1 = max(Probability1))
# fltout_df <- merge(maxout_df, obsOutFinDf,
# by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
# all.x = TRUE)
# fmnout_df <- merge(fltout_df, mnmout_df,
# by.x = c(".pos"), by.y = c(".pos"),
# all.x = TRUE)
# return(fmnout_df)
# }
glbObsOut <- list(NULL
# glbFeatsId will be the first output column, by default
,vars = list()
# ,mapFn = function(obsOutFinDf) {
# }
)
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
# txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
# dplyr::mutate(
# lunch = levels(glbObsTrn[, "lunch" ])[
# round(mean(as.numeric(glbObsTrn[, "lunch" ])), 0)],
# dinner = levels(glbObsTrn[, "dinner" ])[
# round(mean(as.numeric(glbObsTrn[, "dinner" ])), 0)],
# reserve = levels(glbObsTrn[, "reserve" ])[
# round(mean(as.numeric(glbObsTrn[, "reserve" ])), 0)],
# outdoor = levels(glbObsTrn[, "outdoor" ])[
# round(mean(as.numeric(glbObsTrn[, "outdoor" ])), 0)],
# expensive = levels(glbObsTrn[, "expensive"])[
# round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
# liquor = levels(glbObsTrn[, "liquor" ])[
# round(mean(as.numeric(glbObsTrn[, "liquor" ])), 0)],
# table = levels(glbObsTrn[, "table" ])[
# round(mean(as.numeric(glbObsTrn[, "table" ])), 0)],
# classy = levels(glbObsTrn[, "classy" ])[
# round(mean(as.numeric(glbObsTrn[, "classy" ])), 0)],
# kids = levels(glbObsTrn[, "kids" ])[
# round(mean(as.numeric(glbObsTrn[, "kids" ])), 0)]
# )
#
# print("ObsNew output class tables:")
# print(sapply(c("lunch","dinner","reserve","outdoor",
# "expensive","liquor","table",
# "classy","kids"),
# function(feat) table(txfout_df[, feat], useNA = "ifany")))
#
# txfout_df <- txfout_df %>%
# dplyr::mutate(labels = "") %>%
# dplyr::mutate(labels =
# ifelse(lunch != "-1", paste(labels, lunch ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(dinner != "-1", paste(labels, dinner ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(reserve != "-1", paste(labels, reserve ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(outdoor != "-1", paste(labels, outdoor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
# dplyr::mutate(labels =
# ifelse(liquor != "-1", paste(labels, liquor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(table != "-1", paste(labels, table ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(classy != "-1", paste(labels, classy ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(kids != "-1", paste(labels, kids ), labels)) %>%
# dplyr::select(business_id, labels)
# return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
if (glb_is_classification && glb_is_binomial) {
# glbObsOut$vars[["Probability1"]] <-
# "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]"
# glbObsOut$vars[[glb_rsp_var_raw]] <-
# "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
# mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
glbObsOut$vars[["Predictions"]] <-
"%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
# glbObsOut$vars[[glbFeatsId]] <-
# "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
glbObsOut$vars[[glb_rsp_var]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
# for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
# glbObsOut$vars[[outVar]] <-
# paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- # NULL #: default
c("Votes_Ensemble_cnk06_out_fin.csv") # manual stack
# c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack
glbOut <- list(pfx = "Q109244No_AllXpreProc_cnk02_rest_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
,"import.data","inspect.data","scrub.data","transform.data"
,"extract.features"
,"extract.features.datetime","extract.features.image","extract.features.price"
,"extract.features.text","extract.features.string"
,"extract.features.end"
,"manage.missing.data","cluster.data","partition.data.training","select.features"
,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
,"fit.data.training_0","fit.data.training_1"
,"predict.data.new"
,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
!identical(chkChunksLabels, glbChunks$labels)) {
print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s",
setdiff(chkChunksLabels, glbChunks$labels)))
print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s",
setdiff(glbChunks$labels, chkChunksLabels)))
}
glbChunks[["first"]] <- "fit.models_1" # NULL # default: script will load envir from previous chunk
glbChunks[["last" ]] <- NULL # NULL # default: script will save envir at end of this chunk
glbChunks[["inpFilePathName"]] <- "data/Q109244No_AllXpreProc_cnk02_fit.models_1_fit.models_1.RData" # NULL: default or "data/<prvScriptName>_<lstChunkLbl>.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Temporary: Delete this function (if any) from here after appropriate .RData file is saved
# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL,
ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1 1 0 0 9.967 NA NAStep 1.0: fit models_1
chunk option: eval=
Step 1.0: fit models_1
Step 1.0: fit models_1
```{r scrub.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
Step 1.0: fit models_1
```{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: fit models_1
Step 1.0: fit models_1
```{r select.features, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}
Step 1.0: fit models_1
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")
# load(paste0(glbOut$pfx, "dsk.RData"))
glbgetModelSelectFormula <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
glbgetDisplayModelsDf <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(glbgetModelSelectFormula(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#glbgetDisplayModelsDf()
glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
return(df)
}
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
# c("id.prefix", "method", "type",
# # trainControl params
# "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# # train params
# "metric", "metric.maximize", "tune.df")
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indepVar=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
train.method = "glmnet")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")
indepVar <- mygetIndepVar(glb_feats_df)
indepVar <- setdiff(indepVar, unique(glb_feats_df$cor.high.X))
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Low.cor.X",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")
rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)```{r fit.models_1, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 13.244 NA NA## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 13.244 13.253
## 2 fit.models_1_All.X 1 1 setup 13.254 NA
## elapsed
## 1 0.009
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 13.254 13.259
## 3 fit.models_1_All.X 1 2 glmnet 13.260 NA
## elapsed
## 2 0.005
## 3 NA
## [1] "skipping fitting model: All.X##rcv#glmnet"## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 13.260 13.266
## 4 fit.models_1_preProc 1 3 preProc 13.266 NA
## elapsed
## 3 0.006
## 4 NA## Loading required package: gdata## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.## ## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.##
## Attaching package: 'gdata'## The following objects are masked from 'package:dplyr':
##
## combine, first, last## The following object is masked from 'package:stats':
##
## nobs## The following object is masked from 'package:utils':
##
## object.size## id
## All.X##rcv#glmnet All.X##rcv#glmnet
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Random###myrandom_classfr Random###myrandom_classfr
## feats
## All.X##rcv#glmnet Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## Low.cor.X##rcv#glmnet Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## Max.cor.Y.rcv.1X1###glmnet Q115611.fctr,Q113181.fctr
## Max.cor.Y##rcv#rpart Q115611.fctr,Q113181.fctr
## Random###myrandom_classfr .rnorm
## max.nTuningRuns min.elapsedtime.everything
## All.X##rcv#glmnet 25 13.519
## Low.cor.X##rcv#glmnet 25 14.590
## Max.cor.Y.rcv.1X1###glmnet 0 0.766
## Max.cor.Y##rcv#rpart 5 1.468
## Random###myrandom_classfr 0 0.272
## min.elapsedtime.final max.AUCpROC.fit
## All.X##rcv#glmnet 1.332 0.5916814
## Low.cor.X##rcv#glmnet 1.337 0.6219660
## Max.cor.Y.rcv.1X1###glmnet 0.031 0.5862754
## Max.cor.Y##rcv#rpart 0.012 0.5862754
## Random###myrandom_classfr 0.002 0.4824581
## max.Sens.fit max.Spec.fit max.AUCROCR.fit
## All.X##rcv#glmnet 0.3325243 0.8508385 0.6797696
## Low.cor.X##rcv#glmnet 0.3992718 0.8446602 0.6998930
## Max.cor.Y.rcv.1X1###glmnet 0.4029126 0.7696381 0.6223104
## Max.cor.Y##rcv#rpart 0.4029126 0.7696381 0.5949108
## Random###myrandom_classfr 0.3956311 0.5692851 0.5146734
## opt.prob.threshold.fit max.f.score.fit
## All.X##rcv#glmnet 0.55 0.7026116
## Low.cor.X##rcv#glmnet 0.55 0.7180805
## Max.cor.Y.rcv.1X1###glmnet 0.50 0.6984381
## Max.cor.Y##rcv#rpart 0.50 0.6984381
## Random###myrandom_classfr 0.40 0.7333333
## max.Accuracy.fit max.AccuracyLower.fit
## All.X##rcv#glmnet 0.6126764 0.6175160
## Low.cor.X##rcv#glmnet 0.6128487 0.6423517
## Max.cor.Y.rcv.1X1###glmnet 0.6152274 0.5932560
## Max.cor.Y##rcv#rpart 0.6136958 0.5932560
## Random###myrandom_classfr 0.5789474 0.5567125
## max.AccuracyUpper.fit max.Kappa.fit
## All.X##rcv#glmnet 0.6605534 0.1537844
## Low.cor.X##rcv#glmnet 0.6846987 0.1649007
## Max.cor.Y.rcv.1X1###glmnet 0.6368528 0.1794092
## Max.cor.Y##rcv#rpart 0.6368528 0.1758050
## Random###myrandom_classfr 0.6009453 0.0000000
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## All.X##rcv#glmnet 0.5541115 0.3130841 0.7951389
## Low.cor.X##rcv#glmnet 0.5554420 0.3504673 0.7604167
## Max.cor.Y.rcv.1X1###glmnet 0.5468912 0.3785047 0.7152778
## Max.cor.Y##rcv#rpart 0.5468912 0.3785047 0.7152778
## Random###myrandom_classfr 0.4990752 0.4252336 0.5729167
## max.AUCROCR.OOB opt.prob.threshold.OOB
## All.X##rcv#glmnet 0.5814512 0.50
## Low.cor.X##rcv#glmnet 0.5688766 0.50
## Max.cor.Y.rcv.1X1###glmnet 0.5719513 0.55
## Max.cor.Y##rcv#rpart 0.5452362 0.40
## Random###myrandom_classfr 0.5181075 0.40
## max.f.score.OOB max.Accuracy.OOB
## All.X##rcv#glmnet 0.6897590 0.5896414
## Low.cor.X##rcv#glmnet 0.6780186 0.5856574
## Max.cor.Y.rcv.1X1###glmnet 0.6467662 0.5756972
## Max.cor.Y##rcv#rpart 0.7291139 0.5737052
## Random###myrandom_classfr 0.7291139 0.5737052
## max.AccuracyLower.OOB max.AccuracyUpper.OOB
## All.X##rcv#glmnet 0.5451919 0.6330291
## Low.cor.X##rcv#glmnet 0.5411697 0.6291304
## Max.cor.Y.rcv.1X1###glmnet 0.5311268 0.6193710
## Max.cor.Y##rcv#rpart 0.5291204 0.6174170
## Random###myrandom_classfr 0.5291204 0.6174170
## max.Kappa.OOB max.AccuracySD.fit
## All.X##rcv#glmnet 0.1142593 0.010022211
## Low.cor.X##rcv#glmnet 0.1157481 0.009997796
## Max.cor.Y.rcv.1X1###glmnet 0.1182524 NA
## Max.cor.Y##rcv#rpart 0.0000000 0.010619247
## Random###myrandom_classfr 0.0000000 NA
## max.KappaSD.fit
## All.X##rcv#glmnet 0.01937522
## Low.cor.X##rcv#glmnet 0.01935600
## Max.cor.Y.rcv.1X1###glmnet NA
## Max.cor.Y##rcv#rpart 0.02237201
## Random###myrandom_classfr NA## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: All.X#zv#rcv#glmnet"
## [1] " indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.675000 secs"## Loading required package: glmnet## Loading required package: Matrix## Loaded glmnet 2.0-5## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0333 on full training set
## [1] "myfit_mdl: train complete: 15.924000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = bstMdlIdComponents$family, : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 85 -none- numeric
## beta 21760 dgCMatrix S4
## df 85 -none- numeric
## dim 2 -none- numeric
## lambda 85 -none- numeric
## dev.ratio 85 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 256 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.2689370104 0.1223388126
## Hhold.fctrPKn Hhold.fctrPKy
## -0.5431418661 -0.6618709561
## Hhold.fctrSKy Income.fctr.Q
## -0.1090177325 0.0980362635
## Income.fctr.C Income.fctr^6
## 0.1010397569 -0.0558545740
## Q100562.fctrNo Q100689.fctrYes
## -0.1227211571 -0.0242725337
## Q101163.fctrDad Q102089.fctrOwn
## 0.1609136964 0.0213110713
## Q104996.fctrYes Q105655.fctrYes
## -0.0733859891 0.0994923204
## Q106272.fctrNo Q106272.fctrYes
## -0.0985282958 0.0109728198
## Q106388.fctrYes Q106997.fctrGr
## 0.0222597292 0.1722153193
## Q106997.fctrYy Q108855.fctrUmm...
## -0.0005482815 -0.0640940960
## Q108855.fctrYes! Q109367.fctrNo
## 0.0043637645 0.0776512027
## Q109367.fctrYes Q110740.fctrPC
## -0.0232319681 0.0617129947
## Q112512.fctrNo Q113181.fctrNo
## -0.0089942731 -0.2565348387
## Q113181.fctrYes Q114386.fctrMysterious
## 0.0883115008 -0.0488277360
## Q114517.fctrNo Q115390.fctrNo
## -0.0891692654 0.0037303385
## Q115611.fctrNo Q115611.fctrYes
## -0.0867549557 0.2945956632
## Q115899.fctrCs Q116601.fctrNo
## -0.0700586145 -0.0530411889
## Q116881.fctrHappy Q116881.fctrRight
## -0.0660674927 0.2077280095
## Q116953.fctrNo Q117193.fctrStandard hours
## 0.0531229393 0.0358169094
## Q118892.fctrNo Q119650.fctrGiving
## -0.0768159368 0.1151091086
## Q120194.fctrStudy first Q120379.fctrYes
## -0.0034275168 -0.0337302239
## Q120472.fctrScience Q122769.fctrYes
## 0.0750873634 0.0270370204
## Q122771.fctrPt Q123464.fctrYes
## 0.0093972520 0.0018784552
## Q123621.fctrYes Q124742.fctrNo
## 0.0702491183 -0.1091270840
## Q96024.fctrNo Q98197.fctrNo
## -0.0097694988 -0.1666944739
## Q98869.fctrNo Q99480.fctrNo
## -0.1988235708 -0.1985567368
## Q99480.fctrYes Q99716.fctrYes
## 0.0163095000 -0.0848727311
## Hhold.fctrSKn:.clusterid.fctr2 Hhold.fctrSKy:.clusterid.fctr3
## 0.0972361315 -0.2486718925
## Hhold.fctrN:.clusterid.fctr4 Hhold.fctrMKy:.clusterid.fctr4
## 0.2316323732 0.0799692788
## Hhold.fctrPKy:.clusterid.fctr4 YOB.Age.fctr(25,30]:YOB.Age.dff
## 1.0850351271 0.0174906316
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.0338654783
## [1] "max lambda < lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.2687574078 0.1268980348
## Hhold.fctrPKn Hhold.fctrPKy
## -0.5667951509 -0.7360179842
## Hhold.fctrSKy Income.fctr.Q
## -0.1223613415 0.1076270135
## Income.fctr.C Income.fctr^6
## 0.1181824886 -0.0677662807
## Q100562.fctrNo Q100689.fctrYes
## -0.1336075822 -0.0361502891
## Q101163.fctrDad Q102089.fctrOwn
## 0.1686107441 0.0295085694
## Q104996.fctrYes Q105655.fctrYes
## -0.0849702314 0.1075000884
## Q106272.fctrNo Q106272.fctrYes
## -0.1055147182 0.0127575171
## Q106388.fctrYes Q106997.fctrGr
## 0.0276876095 0.1820281443
## Q106997.fctrYy Q108855.fctrUmm...
## -0.0075273343 -0.0728898757
## Q108855.fctrYes! Q109367.fctrNo
## 0.0041288338 0.0844117618
## Q109367.fctrYes Q110740.fctrPC
## -0.0244431062 0.0676957492
## Q112512.fctrNo Q113181.fctrNo
## -0.0209172822 -0.2616295419
## Q113181.fctrYes Q114386.fctrMysterious
## 0.0851082794 -0.0595585781
## Q114517.fctrNo Q115390.fctrNo
## -0.1000702347 0.0122548916
## Q115611.fctrNo Q115611.fctrYes
## -0.0839508900 0.3033680414
## Q115899.fctrCs Q116197.fctrA.M.
## -0.0787815553 0.0025657763
## Q116601.fctrNo Q116881.fctrHappy
## -0.0643774543 -0.0725092183
## Q116881.fctrRight Q116953.fctrNo
## 0.2118371133 0.0609143935
## Q117193.fctrStandard hours Q118233.fctrYes
## 0.0462755734 -0.0004803217
## Q118892.fctrNo Q119650.fctrGiving
## -0.0889319734 0.1258648670
## Q120194.fctrStudy first Q120379.fctrYes
## -0.0111318453 -0.0460010967
## Q120472.fctrScience Q122120.fctrYes
## 0.0857212012 0.0087862652
## Q122769.fctrYes Q122771.fctrPt
## 0.0340947285 0.0181767014
## Q123464.fctrYes Q123621.fctrYes
## 0.0299377349 0.0757128321
## Q124742.fctrNo Q96024.fctrNo
## -0.1201268773 -0.0136151428
## Q98059.fctrYes Q98197.fctrNo
## -0.0008977055 -0.1704429347
## Q98869.fctrNo Q99480.fctrNo
## -0.2057924299 -0.2024638922
## Q99480.fctrYes Q99716.fctrYes
## 0.0226409850 -0.0981662351
## Hhold.fctrPKy:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr2
## -0.0058086382 0.1098872900
## Hhold.fctrSKy:.clusterid.fctr3 Hhold.fctrN:.clusterid.fctr4
## -0.2880193501 0.2766211491
## Hhold.fctrMKy:.clusterid.fctr4 Hhold.fctrPKy:.clusterid.fctr4
## 0.0932839092 1.2878098547
## YOB.Age.fctr(25,30]:YOB.Age.dff YOB.Age.fctr(35,40]:YOB.Age.dff
## 0.0216901093 -0.0381595075
## [1] "myfit_mdl: train diagnostics complete: 16.448000 secs"## Loading required namespace: pROC## Loading required package: ROCR## Loading required package: gplots##
## Attaching package: 'gplots'## The following object is masked from 'package:stats':
##
## lowess
## Loading required package: sqldf## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLite## Loading required package: DBI## Loading required package: tcltk
## Prediction
## Reference D R
## D 461 363
## R 295 838
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.637711e-01 3.024994e-01 6.423517e-01 6.846987e-01 5.789474e-01
## AccuracyPValue McnemarPValue
## 8.843720e-15 9.003218e-03
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy
## 10 0.45 0.7078652 0.5856574
## 11 0.50 0.6780186 0.5856574
## Prediction
## Reference D R
## D 75 139
## R 69 219
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.856574e-01 1.157481e-01 5.411697e-01 6.291304e-01 5.737052e-01
## AccuracyPValue McnemarPValue
## 3.104270e-01 1.715935e-06
## [1] "myfit_mdl: predict complete: 31.041000 secs"
## id
## 1 All.X#zv#rcv#glmnet
## feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 14.938 1.421
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.621966 0.3992718 0.8446602 0.699893
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.55 0.7180805 0.6128487
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6423517 0.6846987 0.1649007
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.555442 0.3504673 0.7604167 0.5688766
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.6780186 0.5856574
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5411697 0.6291304 0.1157481
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009997796 0.019356
## [1] "myfit_mdl: exit: 31.335000 secs"
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: All.X#nzv#rcv#glmnet"
## [1] " indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.692000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.0333 on full training set
## [1] "myfit_mdl: train complete: 18.761000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = bstMdlIdComponents$family, : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 81 -none- numeric
## beta 18792 dgCMatrix S4
## df 81 -none- numeric
## dim 2 -none- numeric
## lambda 81 -none- numeric
## dev.ratio 81 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 232 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.2565760820 0.1125625578
## Income.fctr.Q Q100562.fctrNo
## 0.0200881983 -0.0400552280
## Q101163.fctrDad Q105655.fctrYes
## 0.1114222972 0.0415974299
## Q106272.fctrNo Q106997.fctrGr
## -0.0350825878 0.0705821294
## Q109367.fctrNo Q109367.fctrYes
## 0.0355563673 -0.0009344203
## Q110740.fctrPC Q113181.fctrNo
## 0.0192606125 -0.2538213951
## Q113181.fctrYes Q114517.fctrNo
## 0.0854754192 -0.0085934803
## Q115611.fctrNo Q115611.fctrYes
## -0.0832291029 0.2685997936
## Q115899.fctrCs Q116881.fctrHappy
## -0.0133073619 -0.0135347230
## Q116881.fctrRight Q119650.fctrGiving
## 0.1949058077 0.0453286052
## Q120472.fctrScience Q123621.fctrYes
## 0.0049214276 0.0250694369
## Q124742.fctrNo Q98197.fctrNo
## -0.0340011366 -0.1477656293
## Q98869.fctrNo Q99480.fctrNo
## -0.1550703264 -0.1523444492
## Hhold.fctrSKn:.clusterid.fctr2
## 0.0050961491
## [1] "max lambda < lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.246963564 0.126012884
## Income.fctr.Q Q100562.fctrNo
## 0.039957505 -0.056190085
## Q101163.fctrDad Q104996.fctrYes
## 0.123431947 -0.010959019
## Q105655.fctrYes Q106272.fctrNo
## 0.057402644 -0.048879633
## Q106997.fctrGr Q108855.fctrUmm...
## 0.090516881 -0.010817275
## Q109367.fctrNo Q109367.fctrYes
## 0.047480481 -0.007017792
## Q110740.fctrPC Q113181.fctrNo
## 0.031675542 -0.259122410
## Q113181.fctrYes Q114517.fctrNo
## 0.082276233 -0.026920366
## Q115611.fctrNo Q115611.fctrYes
## -0.082696330 0.277749314
## Q115899.fctrCs Q116881.fctrHappy
## -0.025710723 -0.025669655
## Q116881.fctrRight Q118892.fctrNo
## 0.199437752 -0.002586438
## Q119650.fctrGiving Q120472.fctrScience
## 0.059960865 0.016348743
## Q123621.fctrYes Q124742.fctrNo
## 0.031501643 -0.051308646
## Q98197.fctrNo Q98869.fctrNo
## -0.151551929 -0.168787585
## Q99480.fctrNo Hhold.fctrSKn:.clusterid.fctr2
## -0.164316195 0.030388848
## Hhold.fctrMKy:.clusterid.fctr4
## 0.007961082
## [1] "myfit_mdl: train diagnostics complete: 19.416000 secs"
## Prediction
## Reference D R
## D 402 422
## R 305 828
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.285130e-01 2.229848e-01 6.066695e-01 6.499708e-01 5.789474e-01
## AccuracyPValue McnemarPValue
## 4.406192e-06 1.691091e-05
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy
## 10 0.45 0.7167785 0.5796813
## 11 0.50 0.6827068 0.5796813## Prediction
## Reference D R
## D 64 150
## R 61 227
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.796813e-01 9.218533e-02 5.351418e-01 6.232769e-01 5.737052e-01
## AccuracyPValue McnemarPValue
## 4.115799e-01 1.376790e-09
## [1] "myfit_mdl: predict complete: 29.138000 secs"
## id
## 1 All.X#nzv#rcv#glmnet
## feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 17.988 2.055
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5833517 0.3167476 0.8499559 0.6669664
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.55 0.6949224 0.608759
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6066695 0.6499708 0.1427891
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5436299 0.2990654 0.7881944 0.5784657
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.6827068 0.5796813
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5351418 0.6232769 0.09218533
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01208616 0.02260378
## [1] "myfit_mdl: exit: 29.425000 secs"
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X#BoxCox#rcv#glmnet"
## [1] " indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.665000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0333 on full training set## Warning in is.na(lam): is.na() applied to non-(list or vector) of type
## 'NULL'
## [1] "myfit_mdl: train complete: 18.351000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = bstMdlIdComponents$family, : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 85 -none- numeric
## beta 22100 dgCMatrix S4
## df 85 -none- numeric
## dim 2 -none- numeric
## lambda 85 -none- numeric
## dev.ratio 85 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 260 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.2689370104 0.1223388126
## Hhold.fctrPKn Hhold.fctrPKy
## -0.5431418661 -0.6618709561
## Hhold.fctrSKy Income.fctr.Q
## -0.1090177325 0.0980362635
## Income.fctr.C Income.fctr^6
## 0.1010397569 -0.0558545740
## Q100562.fctrNo Q100689.fctrYes
## -0.1227211571 -0.0242725337
## Q101163.fctrDad Q102089.fctrOwn
## 0.1609136964 0.0213110713
## Q104996.fctrYes Q105655.fctrYes
## -0.0733859891 0.0994923204
## Q106272.fctrNo Q106272.fctrYes
## -0.0985282958 0.0109728198
## Q106388.fctrYes Q106997.fctrGr
## 0.0222597292 0.1722153193
## Q106997.fctrYy Q108855.fctrUmm...
## -0.0005482815 -0.0640940960
## Q108855.fctrYes! Q109367.fctrNo
## 0.0043637645 0.0776512027
## Q109367.fctrYes Q110740.fctrPC
## -0.0232319681 0.0617129947
## Q112512.fctrNo Q113181.fctrNo
## -0.0089942731 -0.2565348387
## Q113181.fctrYes Q114386.fctrMysterious
## 0.0883115008 -0.0488277360
## Q114517.fctrNo Q115390.fctrNo
## -0.0891692654 0.0037303385
## Q115611.fctrNo Q115611.fctrYes
## -0.0867549557 0.2945956632
## Q115899.fctrCs Q116601.fctrNo
## -0.0700586145 -0.0530411889
## Q116881.fctrHappy Q116881.fctrRight
## -0.0660674927 0.2077280095
## Q116953.fctrNo Q117193.fctrStandard hours
## 0.0531229393 0.0358169094
## Q118892.fctrNo Q119650.fctrGiving
## -0.0768159368 0.1151091086
## Q120194.fctrStudy first Q120379.fctrYes
## -0.0034275168 -0.0337302239
## Q120472.fctrScience Q122769.fctrYes
## 0.0750873634 0.0270370204
## Q122771.fctrPt Q123464.fctrYes
## 0.0093972520 0.0018784552
## Q123621.fctrYes Q124742.fctrNo
## 0.0702491183 -0.1091270840
## Q96024.fctrNo Q98197.fctrNo
## -0.0097694988 -0.1666944739
## Q98869.fctrNo Q99480.fctrNo
## -0.1988235708 -0.1985567368
## Q99480.fctrYes Q99716.fctrYes
## 0.0163095000 -0.0848727311
## Hhold.fctrSKn:.clusterid.fctr2 Hhold.fctrSKy:.clusterid.fctr3
## 0.0972361315 -0.2486718925
## Hhold.fctrN:.clusterid.fctr4 Hhold.fctrMKy:.clusterid.fctr4
## 0.2316323732 0.0799692788
## Hhold.fctrPKy:.clusterid.fctr4 YOB.Age.fctr(25,30]:YOB.Age.dff
## 1.0850351271 0.0174906316
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.0338654783
## [1] "max lambda < lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.2687574078 0.1268980348
## Hhold.fctrPKn Hhold.fctrPKy
## -0.5667951509 -0.7360179842
## Hhold.fctrSKy Income.fctr.Q
## -0.1223613415 0.1076270135
## Income.fctr.C Income.fctr^6
## 0.1181824886 -0.0677662807
## Q100562.fctrNo Q100689.fctrYes
## -0.1336075822 -0.0361502891
## Q101163.fctrDad Q102089.fctrOwn
## 0.1686107441 0.0295085694
## Q104996.fctrYes Q105655.fctrYes
## -0.0849702314 0.1075000884
## Q106272.fctrNo Q106272.fctrYes
## -0.1055147182 0.0127575171
## Q106388.fctrYes Q106997.fctrGr
## 0.0276876095 0.1820281443
## Q106997.fctrYy Q108855.fctrUmm...
## -0.0075273343 -0.0728898757
## Q108855.fctrYes! Q109367.fctrNo
## 0.0041288338 0.0844117618
## Q109367.fctrYes Q110740.fctrPC
## -0.0244431062 0.0676957492
## Q112512.fctrNo Q113181.fctrNo
## -0.0209172822 -0.2616295419
## Q113181.fctrYes Q114386.fctrMysterious
## 0.0851082794 -0.0595585781
## Q114517.fctrNo Q115390.fctrNo
## -0.1000702347 0.0122548916
## Q115611.fctrNo Q115611.fctrYes
## -0.0839508900 0.3033680414
## Q115899.fctrCs Q116197.fctrA.M.
## -0.0787815553 0.0025657763
## Q116601.fctrNo Q116881.fctrHappy
## -0.0643774543 -0.0725092183
## Q116881.fctrRight Q116953.fctrNo
## 0.2118371133 0.0609143935
## Q117193.fctrStandard hours Q118233.fctrYes
## 0.0462755734 -0.0004803217
## Q118892.fctrNo Q119650.fctrGiving
## -0.0889319734 0.1258648670
## Q120194.fctrStudy first Q120379.fctrYes
## -0.0111318453 -0.0460010967
## Q120472.fctrScience Q122120.fctrYes
## 0.0857212012 0.0087862652
## Q122769.fctrYes Q122771.fctrPt
## 0.0340947285 0.0181767014
## Q123464.fctrYes Q123621.fctrYes
## 0.0299377349 0.0757128321
## Q124742.fctrNo Q96024.fctrNo
## -0.1201268773 -0.0136151428
## Q98059.fctrYes Q98197.fctrNo
## -0.0008977055 -0.1704429347
## Q98869.fctrNo Q99480.fctrNo
## -0.2057924299 -0.2024638922
## Q99480.fctrYes Q99716.fctrYes
## 0.0226409850 -0.0981662351
## Hhold.fctrPKy:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr2
## -0.0058086382 0.1098872900
## Hhold.fctrSKy:.clusterid.fctr3 Hhold.fctrN:.clusterid.fctr4
## -0.2880193501 0.2766211491
## Hhold.fctrMKy:.clusterid.fctr4 Hhold.fctrPKy:.clusterid.fctr4
## 0.0932839092 1.2878098547
## YOB.Age.fctr(25,30]:YOB.Age.dff YOB.Age.fctr(35,40]:YOB.Age.dff
## 0.0216901093 -0.0381595075
## [1] "myfit_mdl: train diagnostics complete: 18.990000 secs"## Warning in is.na(lam): is.na() applied to non-(list or vector) of type
## 'NULL'## Warning in is.na(lam): is.na() applied to non-(list or vector) of type
## 'NULL'
## Prediction
## Reference D R
## D 461 363
## R 295 838
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.637711e-01 3.024994e-01 6.423517e-01 6.846987e-01 5.789474e-01
## AccuracyPValue McnemarPValue
## 8.843720e-15 9.003218e-03## Warning in is.na(lam): is.na() applied to non-(list or vector) of type
## 'NULL'
## Warning in is.na(lam): is.na() applied to non-(list or vector) of type
## 'NULL'
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy
## 10 0.45 0.7078652 0.5856574
## 11 0.50 0.6780186 0.5856574## Prediction
## Reference D R
## D 75 139
## R 69 219
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.856574e-01 1.157481e-01 5.411697e-01 6.291304e-01 5.737052e-01
## AccuracyPValue McnemarPValue
## 3.104270e-01 1.715935e-06
## [1] "myfit_mdl: predict complete: 28.435000 secs"
## id
## 1 All.X#BoxCox#rcv#glmnet
## feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 17.604 1.832
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.621966 0.3992718 0.8446602 0.699893
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.55 0.7180805 0.6128487
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6423517 0.6846987 0.1649007
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.555442 0.3504673 0.7604167 0.5688766
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.6780186 0.5856574
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5411697 0.6291304 0.1157481
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009997796 0.019356## Warning in is.na(lam): is.na() applied to non-(list or vector) of type
## 'NULL'
## [1] "myfit_mdl: exit: 28.710000 secs"
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X#YeoJohnson#rcv#glmnet"
## [1] " indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.686000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0333 on full training set
## [1] "myfit_mdl: train complete: 45.991000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = bstMdlIdComponents$family, : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 72 -none- numeric
## beta 18720 dgCMatrix S4
## df 72 -none- numeric
## dim 2 -none- numeric
## lambda 72 -none- numeric
## dev.ratio 72 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 260 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.284927345 0.119272354
## Hhold.fctrPKn Hhold.fctrPKy
## -0.541978160 -0.659345299
## Hhold.fctrSKy Income.fctr.Q
## -0.105821337 0.100955662
## Income.fctr.C Income.fctr^6
## 0.102746071 -0.058350533
## Q100562.fctrNo Q100689.fctrYes
## -0.120257921 -0.025994347
## Q101163.fctrDad Q102089.fctrOwn
## 0.160735509 0.020752064
## Q104996.fctrYes Q105655.fctrYes
## -0.072504900 0.098132410
## Q106272.fctrNo Q106272.fctrYes
## -0.099497687 0.010039146
## Q106388.fctrYes Q106997.fctrGr
## 0.022279069 0.174070813
## Q108855.fctrUmm... Q108855.fctrYes!
## -0.063819781 0.003789046
## Q109367.fctrNo Q109367.fctrYes
## 0.076878973 -0.023983712
## Q110740.fctrPC Q112512.fctrNo
## 0.062355489 -0.008754679
## Q113181.fctrNo Q113181.fctrYes
## -0.256638975 0.089322239
## Q114386.fctrMysterious Q114517.fctrNo
## -0.049677606 -0.089177737
## Q115390.fctrNo Q115611.fctrNo
## 0.003587387 -0.086533053
## Q115611.fctrYes Q115899.fctrCs
## 0.295161509 -0.068994241
## Q116601.fctrNo Q116881.fctrHappy
## -0.053177435 -0.066789514
## Q116881.fctrRight Q116953.fctrNo
## 0.206032424 0.054073789
## Q117193.fctrStandard hours Q118892.fctrNo
## 0.034183628 -0.076177980
## Q119650.fctrGiving Q120194.fctrStudy first
## 0.115005528 -0.005110625
## Q120379.fctrYes Q120472.fctrScience
## -0.035675231 0.075405834
## Q122769.fctrYes Q122771.fctrPt
## 0.028474850 0.011193207
## Q123621.fctrYes Q124742.fctrNo
## 0.072433901 -0.109460808
## Q96024.fctrNo Q98197.fctrNo
## -0.010054458 -0.166700817
## Q98869.fctrNo Q99480.fctrNo
## -0.201379948 -0.198393462
## Q99480.fctrYes Q99716.fctrYes
## 0.017604588 -0.081940751
## Hhold.fctrSKn:.clusterid.fctr2 Hhold.fctrSKy:.clusterid.fctr3
## 0.093618272 -0.254655380
## Hhold.fctrN:.clusterid.fctr4 Hhold.fctrMKy:.clusterid.fctr4
## 0.232902682 0.079966179
## Hhold.fctrPKy:.clusterid.fctr4 YOB.Age.fctr(25,30]:YOB.Age.dff
## 1.095638283 0.256139412
## YOB.Age.fctr(35,40]:YOB.Age.dff YOB.Age.fctr(65,90]:YOB.Age.dff
## -0.711972337 -0.100447946
## [1] "max lambda < lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 2.869882e-01 1.238080e-01
## Hhold.fctrPKn Hhold.fctrPKy
## -5.658864e-01 -7.334346e-01
## Hhold.fctrSKy Income.fctr.Q
## -1.190002e-01 1.097084e-01
## Income.fctr.C Income.fctr^6
## 1.195921e-01 -6.933467e-02
## Q100562.fctrNo Q100689.fctrYes
## -1.291741e-01 -3.807363e-02
## Q101163.fctrDad Q102089.fctrOwn
## 1.680902e-01 2.947707e-02
## Q104996.fctrYes Q105655.fctrYes
## -8.390703e-02 1.053847e-01
## Q106272.fctrNo Q106272.fctrYes
## -1.065513e-01 1.210340e-02
## Q106388.fctrYes Q106997.fctrGr
## 2.737495e-02 1.839456e-01
## Q106997.fctrYy Q108855.fctrUmm...
## -6.655299e-03 -7.288273e-02
## Q108855.fctrYes! Q109367.fctrNo
## 3.086192e-03 8.388669e-02
## Q109367.fctrYes Q110740.fctrPC
## -2.518669e-02 6.844294e-02
## Q112512.fctrNo Q113181.fctrNo
## -2.052338e-02 -2.613175e-01
## Q113181.fctrYes Q114386.fctrMysterious
## 8.659292e-02 -6.083107e-02
## Q114517.fctrNo Q115390.fctrNo
## -1.002576e-01 1.219109e-02
## Q115611.fctrNo Q115611.fctrYes
## -8.394652e-02 3.036817e-01
## Q115899.fctrCs Q116197.fctrA.M.
## -7.788031e-02 4.033698e-03
## Q116601.fctrNo Q116881.fctrHappy
## -6.510834e-02 -7.328888e-02
## Q116881.fctrRight Q116953.fctrNo
## 2.099052e-01 6.213052e-02
## Q117193.fctrStandard hours Q118233.fctrYes
## 4.433145e-02 -4.370050e-05
## Q118892.fctrNo Q119650.fctrGiving
## -8.873265e-02 1.259459e-01
## Q120194.fctrStudy first Q120379.fctrYes
## -1.296647e-02 -4.853490e-02
## Q120472.fctrScience Q122120.fctrYes
## 8.639695e-02 8.588776e-03
## Q122769.fctrYes Q122771.fctrPt
## 3.581173e-02 2.050404e-02
## Q123464.fctrYes Q123621.fctrYes
## 2.693998e-02 7.735505e-02
## Q124742.fctrNo Q96024.fctrNo
## -1.201438e-01 -1.438883e-02
## Q98059.fctrYes Q98197.fctrNo
## -1.841926e-06 -1.709835e-01
## Q98869.fctrNo Q99480.fctrNo
## -2.089005e-01 -2.030808e-01
## Q99480.fctrYes Q99716.fctrYes
## 2.361771e-02 -9.427593e-02
## Hhold.fctrPKy:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr2
## -9.627428e-03 1.050616e-01
## Hhold.fctrSKy:.clusterid.fctr3 Hhold.fctrN:.clusterid.fctr4
## -2.931047e-01 2.776786e-01
## Hhold.fctrMKy:.clusterid.fctr4 Hhold.fctrPKy:.clusterid.fctr4
## 9.287322e-02 1.298769e+00
## YOB.Age.fctr(25,30]:YOB.Age.dff YOB.Age.fctr(35,40]:YOB.Age.dff
## 3.475568e-01 -8.185835e-01
## YOB.Age.fctr(65,90]:YOB.Age.dff
## -2.706222e-01
## [1] "myfit_mdl: train diagnostics complete: 46.645000 secs"
## Prediction
## Reference D R
## D 332 492
## R 173 960
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.601942e-01 2.641663e-01 6.387259e-01 6.811813e-01 5.789474e-01
## AccuracyPValue McnemarPValue
## 1.087820e-13 6.128653e-35
## Prediction
## Reference D R
## D 45 169
## R 38 250
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.876494e-01 8.502245e-02 5.431804e-01 6.310801e-01 5.737052e-01
## AccuracyPValue McnemarPValue
## 2.792382e-01 1.630685e-19
## [1] "myfit_mdl: predict complete: 56.725000 secs"
## id
## 1 All.X#YeoJohnson#rcv#glmnet
## feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 45.222 2.802
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6251103 0.4029126 0.847308 0.6996691
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.7427466 0.6109767
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6387259 0.6811813 0.1605919
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.555442 0.3504673 0.7604167 0.5696554
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.45 0.7072136 0.5876494
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5431804 0.6310801 0.08502245
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01175425 0.02458174
## [1] "myfit_mdl: exit: 57.035000 secs"
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X#expoTrans#rcv#glmnet"
## [1] " indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.684000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0333 on full training set
## [1] "myfit_mdl: train complete: 51.916000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = bstMdlIdComponents$family, : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 78 -none- numeric
## beta 20280 dgCMatrix S4
## df 78 -none- numeric
## dim 2 -none- numeric
## lambda 78 -none- numeric
## dev.ratio 78 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 260 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.2841057914 0.1193123419
## Hhold.fctrPKn Hhold.fctrPKy
## -0.5419855285 -0.6593516732
## Hhold.fctrSKy Income.fctr.Q
## -0.1058255161 0.1011437818
## Income.fctr.C Income.fctr^6
## 0.1025918337 -0.0589454609
## Q100562.fctrNo Q100689.fctrYes
## -0.1207153114 -0.0260094379
## Q101163.fctrDad Q102089.fctrOwn
## 0.1607740041 0.0207063433
## Q104996.fctrYes Q105655.fctrYes
## -0.0725469025 0.0983156982
## Q106272.fctrNo Q106272.fctrYes
## -0.0994253553 0.0099448131
## Q106388.fctrYes Q106997.fctrGr
## 0.0223568594 0.1740218958
## Q106997.fctrYy Q108855.fctrUmm...
## -0.0001090684 -0.0637276800
## Q108855.fctrYes! Q109367.fctrNo
## 0.0039200713 0.0768614996
## Q109367.fctrYes Q110740.fctrPC
## -0.0240088117 0.0623116526
## Q112512.fctrNo Q113181.fctrNo
## -0.0088673308 -0.2565048866
## Q113181.fctrYes Q114386.fctrMysterious
## 0.0893498023 -0.0495496925
## Q114517.fctrNo Q115390.fctrNo
## -0.0890981420 0.0036314089
## Q115611.fctrNo Q115611.fctrYes
## -0.0869267830 0.2948002245
## Q115899.fctrCs Q116601.fctrNo
## -0.0689354439 -0.0530821269
## Q116881.fctrHappy Q116881.fctrRight
## -0.0667045782 0.2061025675
## Q116953.fctrNo Q117193.fctrStandard hours
## 0.0540600979 0.0342516834
## Q118892.fctrNo Q119650.fctrGiving
## -0.0761324315 0.1149379921
## Q120194.fctrStudy first Q120379.fctrYes
## -0.0050941795 -0.0355275668
## Q120472.fctrScience Q122769.fctrYes
## 0.0753506114 0.0284281170
## Q122771.fctrPt Q123621.fctrYes
## 0.0111313809 0.0725146576
## Q124742.fctrNo Q96024.fctrNo
## -0.1095448086 -0.0099844887
## Q98197.fctrNo Q98869.fctrNo
## -0.1666755762 -0.2012088433
## Q99480.fctrNo Q99480.fctrYes
## -0.1982745942 0.0175707860
## Q99716.fctrYes Hhold.fctrSKn:.clusterid.fctr2
## -0.0822700068 0.0939410343
## Hhold.fctrSKy:.clusterid.fctr3 Hhold.fctrN:.clusterid.fctr4
## -0.2546339394 0.2330197519
## Hhold.fctrMKy:.clusterid.fctr4 Hhold.fctrPKy:.clusterid.fctr4
## 0.0800245288 1.0953376134
## YOB.Age.fctr(25,30]:YOB.Age.dff YOB.Age.fctr(35,40]:YOB.Age.dff
## 0.1104720767 -0.3073477003
## YOB.Age.fctr(65,90]:YOB.Age.dff
## -0.0165829269
## [1] "max lambda < lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.286099539 0.123866450
## Hhold.fctrPKn Hhold.fctrPKy
## -0.565907791 -0.733229779
## Hhold.fctrSKy Income.fctr.Q
## -0.118975950 0.109961892
## Income.fctr.C Income.fctr^6
## 0.119436048 -0.069922780
## Q100562.fctrNo Q100689.fctrYes
## -0.129638771 -0.038066401
## Q101163.fctrDad Q102089.fctrOwn
## 0.168120680 0.029438619
## Q104996.fctrYes Q105655.fctrYes
## -0.083956148 0.105571385
## Q106272.fctrNo Q106272.fctrYes
## -0.106503777 0.011986175
## Q106388.fctrYes Q106997.fctrGr
## 0.027443429 0.183915354
## Q106997.fctrYy Q108855.fctrUmm...
## -0.006769732 -0.072812376
## Q108855.fctrYes! Q109367.fctrNo
## 0.003223047 0.083838223
## Q109367.fctrYes Q110740.fctrPC
## -0.025208887 0.068401874
## Q112512.fctrNo Q113181.fctrNo
## -0.020616649 -0.261338314
## Q113181.fctrYes Q114386.fctrMysterious
## 0.086438169 -0.060692532
## Q114517.fctrNo Q115390.fctrNo
## -0.100200442 0.012224968
## Q115611.fctrNo Q115611.fctrYes
## -0.084025537 0.303586230
## Q115899.fctrCs Q116197.fctrA.M.
## -0.077836087 0.003834848
## Q116601.fctrNo Q116881.fctrHappy
## -0.065025411 -0.073288569
## Q116881.fctrRight Q116953.fctrNo
## 0.209927052 0.062117366
## Q117193.fctrStandard hours Q118892.fctrNo
## 0.044421041 -0.088697392
## Q119650.fctrGiving Q120194.fctrStudy first
## 0.125868938 -0.012947160
## Q120379.fctrYes Q120472.fctrScience
## -0.048381410 0.086353581
## Q122120.fctrYes Q122769.fctrYes
## 0.008665692 0.035757162
## Q122771.fctrPt Q123464.fctrYes
## 0.020400870 0.027188248
## Q123621.fctrYes Q124742.fctrNo
## 0.077460675 -0.120231088
## Q96024.fctrNo Q98197.fctrNo
## -0.014317076 -0.170968548
## Q98869.fctrNo Q99480.fctrNo
## -0.208737179 -0.202912835
## Q99480.fctrYes Q99716.fctrYes
## 0.023597972 -0.094612667
## Hhold.fctrPKy:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr2
## -0.010338466 0.105430108
## Hhold.fctrSKy:.clusterid.fctr3 Hhold.fctrN:.clusterid.fctr4
## -0.293205828 0.277748048
## Hhold.fctrMKy:.clusterid.fctr4 Hhold.fctrPKy:.clusterid.fctr4
## 0.092981303 1.298291258
## YOB.Age.fctr(25,30]:YOB.Age.dff YOB.Age.fctr(35,40]:YOB.Age.dff
## 0.149359813 -0.353675713
## YOB.Age.fctr(65,90]:YOB.Age.dff
## -0.063677920
## [1] "myfit_mdl: train diagnostics complete: 52.604000 secs"
## Prediction
## Reference D R
## D 457 367
## R 298 835
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.601942e-01 2.949612e-01 6.387259e-01 6.811813e-01 5.789474e-01
## AccuracyPValue McnemarPValue
## 1.087820e-13 8.366086e-03
## Prediction
## Reference D R
## D 45 169
## R 36 252
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.916335e-01 9.268043e-02 5.472040e-01 6.349773e-01 5.737052e-01
## AccuracyPValue McnemarPValue
## 2.217899e-01 2.991021e-20
## [1] "myfit_mdl: predict complete: 62.517000 secs"
## id
## 1 All.X#expoTrans#rcv#glmnet
## feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 51.149 2.38
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6240622 0.401699 0.8464254 0.6995974
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.55 0.7152034 0.6109764
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6387259 0.6811813 0.1605916
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.555442 0.3504673 0.7604167 0.569704
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.45 0.7108604 0.5916335
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.547204 0.6349773 0.09268043
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01133176 0.02359669
## [1] "myfit_mdl: exit: 62.835000 secs"
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: All.X#center#rcv#glmnet"
## [1] " indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.709000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0333 on full training set
## [1] "myfit_mdl: train complete: 16.270000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = bstMdlIdComponents$family, : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 85 -none- numeric
## beta 22100 dgCMatrix S4
## df 85 -none- numeric
## dim 2 -none- numeric
## lambda 85 -none- numeric
## dev.ratio 85 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 260 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.3353785090 0.1223388126
## Hhold.fctrPKn Hhold.fctrPKy
## -0.5431418661 -0.6618709561
## Hhold.fctrSKy Income.fctr.Q
## -0.1090177325 0.0980362635
## Income.fctr.C Income.fctr^6
## 0.1010397569 -0.0558545740
## Q100562.fctrNo Q100689.fctrYes
## -0.1227211571 -0.0242725337
## Q101163.fctrDad Q102089.fctrOwn
## 0.1609136964 0.0213110713
## Q104996.fctrYes Q105655.fctrYes
## -0.0733859891 0.0994923204
## Q106272.fctrNo Q106272.fctrYes
## -0.0985282958 0.0109728198
## Q106388.fctrYes Q106997.fctrGr
## 0.0222597292 0.1722153193
## Q106997.fctrYy Q108855.fctrUmm...
## -0.0005482815 -0.0640940960
## Q108855.fctrYes! Q109367.fctrNo
## 0.0043637645 0.0776512027
## Q109367.fctrYes Q110740.fctrPC
## -0.0232319681 0.0617129947
## Q112512.fctrNo Q113181.fctrNo
## -0.0089942731 -0.2565348387
## Q113181.fctrYes Q114386.fctrMysterious
## 0.0883115008 -0.0488277360
## Q114517.fctrNo Q115390.fctrNo
## -0.0891692654 0.0037303385
## Q115611.fctrNo Q115611.fctrYes
## -0.0867549557 0.2945956632
## Q115899.fctrCs Q116601.fctrNo
## -0.0700586145 -0.0530411889
## Q116881.fctrHappy Q116881.fctrRight
## -0.0660674927 0.2077280095
## Q116953.fctrNo Q117193.fctrStandard hours
## 0.0531229393 0.0358169094
## Q118892.fctrNo Q119650.fctrGiving
## -0.0768159368 0.1151091086
## Q120194.fctrStudy first Q120379.fctrYes
## -0.0034275168 -0.0337302239
## Q120472.fctrScience Q122769.fctrYes
## 0.0750873634 0.0270370204
## Q122771.fctrPt Q123464.fctrYes
## 0.0093972520 0.0018784552
## Q123621.fctrYes Q124742.fctrNo
## 0.0702491183 -0.1091270840
## Q96024.fctrNo Q98197.fctrNo
## -0.0097694988 -0.1666944739
## Q98869.fctrNo Q99480.fctrNo
## -0.1988235708 -0.1985567368
## Q99480.fctrYes Q99716.fctrYes
## 0.0163095000 -0.0848727311
## Hhold.fctrSKn:.clusterid.fctr2 Hhold.fctrSKy:.clusterid.fctr3
## 0.0972361315 -0.2486718925
## Hhold.fctrN:.clusterid.fctr4 Hhold.fctrMKy:.clusterid.fctr4
## 0.2316323732 0.0799692788
## Hhold.fctrPKy:.clusterid.fctr4 YOB.Age.fctr(25,30]:YOB.Age.dff
## 1.0850351271 0.0174906316
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.0338654783
## [1] "max lambda < lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.3368045290 0.1268980348
## Hhold.fctrPKn Hhold.fctrPKy
## -0.5667951509 -0.7360179842
## Hhold.fctrSKy Income.fctr.Q
## -0.1223613415 0.1076270135
## Income.fctr.C Income.fctr^6
## 0.1181824886 -0.0677662807
## Q100562.fctrNo Q100689.fctrYes
## -0.1336075822 -0.0361502891
## Q101163.fctrDad Q102089.fctrOwn
## 0.1686107441 0.0295085694
## Q104996.fctrYes Q105655.fctrYes
## -0.0849702314 0.1075000884
## Q106272.fctrNo Q106272.fctrYes
## -0.1055147182 0.0127575171
## Q106388.fctrYes Q106997.fctrGr
## 0.0276876095 0.1820281443
## Q106997.fctrYy Q108855.fctrUmm...
## -0.0075273343 -0.0728898757
## Q108855.fctrYes! Q109367.fctrNo
## 0.0041288338 0.0844117618
## Q109367.fctrYes Q110740.fctrPC
## -0.0244431062 0.0676957492
## Q112512.fctrNo Q113181.fctrNo
## -0.0209172822 -0.2616295419
## Q113181.fctrYes Q114386.fctrMysterious
## 0.0851082794 -0.0595585781
## Q114517.fctrNo Q115390.fctrNo
## -0.1000702347 0.0122548916
## Q115611.fctrNo Q115611.fctrYes
## -0.0839508900 0.3033680414
## Q115899.fctrCs Q116197.fctrA.M.
## -0.0787815553 0.0025657763
## Q116601.fctrNo Q116881.fctrHappy
## -0.0643774543 -0.0725092183
## Q116881.fctrRight Q116953.fctrNo
## 0.2118371133 0.0609143935
## Q117193.fctrStandard hours Q118233.fctrYes
## 0.0462755734 -0.0004803217
## Q118892.fctrNo Q119650.fctrGiving
## -0.0889319734 0.1258648670
## Q120194.fctrStudy first Q120379.fctrYes
## -0.0111318453 -0.0460010967
## Q120472.fctrScience Q122120.fctrYes
## 0.0857212012 0.0087862652
## Q122769.fctrYes Q122771.fctrPt
## 0.0340947285 0.0181767014
## Q123464.fctrYes Q123621.fctrYes
## 0.0299377349 0.0757128321
## Q124742.fctrNo Q96024.fctrNo
## -0.1201268773 -0.0136151428
## Q98059.fctrYes Q98197.fctrNo
## -0.0008977055 -0.1704429347
## Q98869.fctrNo Q99480.fctrNo
## -0.2057924299 -0.2024638922
## Q99480.fctrYes Q99716.fctrYes
## 0.0226409850 -0.0981662351
## Hhold.fctrPKy:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr2
## -0.0058086382 0.1098872900
## Hhold.fctrSKy:.clusterid.fctr3 Hhold.fctrN:.clusterid.fctr4
## -0.2880193501 0.2766211491
## Hhold.fctrMKy:.clusterid.fctr4 Hhold.fctrPKy:.clusterid.fctr4
## 0.0932839092 1.2878098547
## YOB.Age.fctr(25,30]:YOB.Age.dff YOB.Age.fctr(35,40]:YOB.Age.dff
## 0.0216901093 -0.0381595075
## [1] "myfit_mdl: train diagnostics complete: 16.923000 secs"
## Prediction
## Reference D R
## D 461 363
## R 295 838
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.637711e-01 3.024994e-01 6.423517e-01 6.846987e-01 5.789474e-01
## AccuracyPValue McnemarPValue
## 8.843720e-15 9.003218e-03
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy
## 10 0.45 0.7078652 0.5856574
## 11 0.50 0.6780186 0.5856574## Prediction
## Reference D R
## D 75 139
## R 69 219
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.856574e-01 1.157481e-01 5.411697e-01 6.291304e-01 5.737052e-01
## AccuracyPValue McnemarPValue
## 3.104270e-01 1.715935e-06
## [1] "myfit_mdl: predict complete: 26.722000 secs"
## id
## 1 All.X#center#rcv#glmnet
## feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 15.479 1.58
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.621966 0.3992718 0.8446602 0.699893
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.55 0.7180805 0.6128487
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6423517 0.6846987 0.1649007
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.555442 0.3504673 0.7604167 0.5688766
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.6780186 0.5856574
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5411697 0.6291304 0.1157481
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009997796 0.019356
## [1] "myfit_mdl: exit: 27.056000 secs"
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X#scale#rcv#glmnet"
## [1] " indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.673000 secs"## Warning in preProcess.default(method = "scale", x =
## structure(c(-0.480112420809766, : These variables have zero variances:
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0333 on full training set## Warning in preProcess.default(thresh = 0.95, k = 5, method = "scale", x
## = structure(c(-0.480112420809766, : These variables have zero variances:
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff
## [1] "myfit_mdl: train complete: 15.588000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = bstMdlIdComponents$family, : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 85 -none- numeric
## beta 22100 dgCMatrix S4
## df 85 -none- numeric
## dim 2 -none- numeric
## lambda 85 -none- numeric
## dev.ratio 85 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 260 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.2689370104 0.0582685657
## Hhold.fctrPKn Hhold.fctrPKy
## -0.0857222952 -0.0557948230
## Hhold.fctrSKy Income.fctr.Q
## -0.0189496586 0.0375248761
## Income.fctr.C Income.fctr^6
## 0.0378139429 -0.0214429930
## Q100562.fctrNo Q100689.fctrYes
## -0.0453982312 -0.0121334702
## Q101163.fctrDad Q102089.fctrOwn
## 0.0804768974 0.0104433630
## Q104996.fctrYes Q105655.fctrYes
## -0.0363894639 0.0497457184
## Q106272.fctrNo Q106272.fctrYes
## -0.0411765569 0.0052928490
## Q106388.fctrYes Q106997.fctrGr
## 0.0095044705 0.0859423441
## Q106997.fctrYy Q108855.fctrUmm...
## -0.0002684511 -0.0304420846
## Q108855.fctrYes! Q109367.fctrNo
## 0.0021809210 0.0379086758
## Q109367.fctrYes Q110740.fctrPC
## -0.0115562742 0.0308013595
## Q112512.fctrNo Q113181.fctrNo
## -0.0033188626 -0.1279277954
## Q113181.fctrYes Q114386.fctrMysterious
## 0.0430621139 -0.0244124521
## Q114517.fctrNo Q115390.fctrNo
## -0.0440510918 0.0017532494
## Q115611.fctrNo Q115611.fctrYes
## -0.0433714261 0.1414192000
## Q115899.fctrCs Q116601.fctrNo
## -0.0329012272 -0.0186047389
## Q116881.fctrHappy Q116881.fctrRight
## -0.0328458949 0.0881059093
## Q116953.fctrNo Q117193.fctrStandard hours
## 0.0232803894 0.0178105863
## Q118892.fctrNo Q119650.fctrGiving
## -0.0366364805 0.0558624455
## Q120194.fctrStudy first Q120379.fctrYes
## -0.0017053109 -0.0158780334
## Q120472.fctrScience Q122769.fctrYes
## 0.0369922916 0.0123436296
## Q122771.fctrPt Q123464.fctrYes
## 0.0033537563 0.0003808286
## Q123621.fctrYes Q124742.fctrNo
## 0.0343880652 -0.0521513512
## Q96024.fctrNo Q98197.fctrNo
## -0.0044886901 -0.0831066391
## Q98869.fctrNo Q99480.fctrNo
## -0.0706569408 -0.0732669015
## Q99480.fctrYes Q99716.fctrYes
## 0.0076123789 -0.0229261739
## Hhold.fctrSKn:.clusterid.fctr2 Hhold.fctrSKy:.clusterid.fctr3
## 0.0340559799 -0.0209627029
## Hhold.fctrN:.clusterid.fctr4 Hhold.fctrMKy:.clusterid.fctr4
## 0.0233023235 0.0208175684
## Hhold.fctrPKy:.clusterid.fctr4 YOB.Age.fctr(25,30]:YOB.Age.dff
## 0.0346778640 0.0193079561
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.0346153145
## [1] "max lambda < lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.2687574078 0.0604400706
## Hhold.fctrPKn Hhold.fctrPKy
## -0.0894554154 -0.0620453168
## Hhold.fctrSKy Income.fctr.Q
## -0.0212690688 0.0411958821
## Income.fctr.C Income.fctr^6
## 0.0442295787 -0.0260159873
## Q100562.fctrNo Q100689.fctrYes
## -0.0494254458 -0.0180709793
## Q101163.fctrDad Q102089.fctrOwn
## 0.0843263803 0.0144604979
## Q104996.fctrYes Q105655.fctrYes
## -0.0421336716 0.0537495668
## Q106272.fctrNo Q106272.fctrYes
## -0.0440962950 0.0061537156
## Q106388.fctrYes Q106997.fctrGr
## 0.0118220695 0.0908393370
## Q106997.fctrYy Q108855.fctrUmm...
## -0.0036855537 -0.0346197217
## Q108855.fctrYes! Q109367.fctrNo
## 0.0020635074 0.0412091249
## Q109367.fctrYes Q110740.fctrPC
## -0.0121587305 0.0337873914
## Q112512.fctrNo Q113181.fctrNo
## -0.0077184208 -0.1304684021
## Q113181.fctrYes Q114386.fctrMysterious
## 0.0415001715 -0.0297775619
## Q114517.fctrNo Q115390.fctrNo
## -0.0494363509 0.0057597672
## Q115611.fctrNo Q115611.fctrYes
## -0.0419695889 0.1456303371
## Q115899.fctrCs Q116197.fctrA.M.
## -0.0369977321 0.0011445068
## Q116601.fctrNo Q116881.fctrHappy
## -0.0225810498 -0.0360484418
## Q116881.fctrRight Q116953.fctrNo
## 0.0898487476 0.0266948858
## Q117193.fctrStandard hours Q118233.fctrYes
## 0.0230113403 -0.0002013763
## Q118892.fctrNo Q119650.fctrGiving
## -0.0424150852 0.0610822146
## Q120194.fctrStudy first Q120379.fctrYes
## -0.0055384870 -0.0216543760
## Q120472.fctrScience Q122120.fctrYes
## 0.0422311228 0.0034867935
## Q122769.fctrYes Q122771.fctrPt
## 0.0155657944 0.0064870270
## Q123464.fctrYes Q123621.fctrYes
## 0.0060694262 0.0370626403
## Q124742.fctrNo Q96024.fctrNo
## -0.0574081038 -0.0062556082
## Q98059.fctrYes Q98197.fctrNo
## -0.0003727267 -0.0849754591
## Q98869.fctrNo Q99480.fctrNo
## -0.0731334996 -0.0747086313
## Q99480.fctrYes Q99716.fctrYes
## 0.0105675685 -0.0265170703
## Hhold.fctrPKy:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr2
## -0.0001313043 0.0384869213
## Hhold.fctrSKy:.clusterid.fctr3 Hhold.fctrN:.clusterid.fctr4
## -0.0242796402 0.0278282151
## Hhold.fctrMKy:.clusterid.fctr4 Hhold.fctrPKy:.clusterid.fctr4
## 0.0242836273 0.0411585707
## YOB.Age.fctr(25,30]:YOB.Age.dff YOB.Age.fctr(35,40]:YOB.Age.dff
## 0.0239437711 -0.0390044204
## [1] "myfit_mdl: train diagnostics complete: 16.257000 secs"
## Prediction
## Reference D R
## D 461 363
## R 295 838
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.637711e-01 3.024994e-01 6.423517e-01 6.846987e-01 5.789474e-01
## AccuracyPValue McnemarPValue
## 8.843720e-15 9.003218e-03
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy
## 10 0.45 0.7078652 0.5856574
## 11 0.50 0.6780186 0.5856574## Prediction
## Reference D R
## D 75 139
## R 69 219
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.856574e-01 1.157481e-01 5.411697e-01 6.291304e-01 5.737052e-01
## AccuracyPValue McnemarPValue
## 3.104270e-01 1.715935e-06
## [1] "myfit_mdl: predict complete: 25.781000 secs"
## id
## 1 All.X#scale#rcv#glmnet
## feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 14.819 1.493
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.621966 0.3992718 0.8446602 0.699893
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.55 0.7180805 0.6128487
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6423517 0.6846987 0.1649007
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.555442 0.3504673 0.7604167 0.5688766
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.6780186 0.5856574
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5411697 0.6291304 0.1157481
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009997796 0.019356
## [1] "myfit_mdl: exit: 26.109000 secs"
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X#center.scale#rcv#glmnet"
## [1] " indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.670000 secs"## Warning in preProcess.default(method = c("center", "scale"), x =
## structure(c(-0.480112420809766, : These variables have zero variances:
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0333 on full training set## Warning in preProcess.default(thresh = 0.95, k = 5, method = c("center", :
## These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff
## [1] "myfit_mdl: train complete: 17.717000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = bstMdlIdComponents$family, : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 85 -none- numeric
## beta 22100 dgCMatrix S4
## df 85 -none- numeric
## dim 2 -none- numeric
## lambda 85 -none- numeric
## dev.ratio 85 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 260 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.3353785090 0.0582685657
## Hhold.fctrPKn Hhold.fctrPKy
## -0.0857222952 -0.0557948230
## Hhold.fctrSKy Income.fctr.Q
## -0.0189496586 0.0375248761
## Income.fctr.C Income.fctr^6
## 0.0378139429 -0.0214429930
## Q100562.fctrNo Q100689.fctrYes
## -0.0453982312 -0.0121334702
## Q101163.fctrDad Q102089.fctrOwn
## 0.0804768974 0.0104433630
## Q104996.fctrYes Q105655.fctrYes
## -0.0363894639 0.0497457184
## Q106272.fctrNo Q106272.fctrYes
## -0.0411765569 0.0052928490
## Q106388.fctrYes Q106997.fctrGr
## 0.0095044705 0.0859423441
## Q106997.fctrYy Q108855.fctrUmm...
## -0.0002684511 -0.0304420846
## Q108855.fctrYes! Q109367.fctrNo
## 0.0021809210 0.0379086758
## Q109367.fctrYes Q110740.fctrPC
## -0.0115562742 0.0308013595
## Q112512.fctrNo Q113181.fctrNo
## -0.0033188626 -0.1279277954
## Q113181.fctrYes Q114386.fctrMysterious
## 0.0430621139 -0.0244124521
## Q114517.fctrNo Q115390.fctrNo
## -0.0440510918 0.0017532494
## Q115611.fctrNo Q115611.fctrYes
## -0.0433714261 0.1414192000
## Q115899.fctrCs Q116601.fctrNo
## -0.0329012272 -0.0186047389
## Q116881.fctrHappy Q116881.fctrRight
## -0.0328458949 0.0881059093
## Q116953.fctrNo Q117193.fctrStandard hours
## 0.0232803894 0.0178105863
## Q118892.fctrNo Q119650.fctrGiving
## -0.0366364805 0.0558624455
## Q120194.fctrStudy first Q120379.fctrYes
## -0.0017053109 -0.0158780334
## Q120472.fctrScience Q122769.fctrYes
## 0.0369922916 0.0123436296
## Q122771.fctrPt Q123464.fctrYes
## 0.0033537563 0.0003808286
## Q123621.fctrYes Q124742.fctrNo
## 0.0343880652 -0.0521513512
## Q96024.fctrNo Q98197.fctrNo
## -0.0044886901 -0.0831066391
## Q98869.fctrNo Q99480.fctrNo
## -0.0706569408 -0.0732669015
## Q99480.fctrYes Q99716.fctrYes
## 0.0076123789 -0.0229261739
## Hhold.fctrSKn:.clusterid.fctr2 Hhold.fctrSKy:.clusterid.fctr3
## 0.0340559799 -0.0209627029
## Hhold.fctrN:.clusterid.fctr4 Hhold.fctrMKy:.clusterid.fctr4
## 0.0233023235 0.0208175684
## Hhold.fctrPKy:.clusterid.fctr4 YOB.Age.fctr(25,30]:YOB.Age.dff
## 0.0346778640 0.0193079561
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.0346153145
## [1] "max lambda < lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.3368045290 0.0604400706
## Hhold.fctrPKn Hhold.fctrPKy
## -0.0894554154 -0.0620453168
## Hhold.fctrSKy Income.fctr.Q
## -0.0212690688 0.0411958821
## Income.fctr.C Income.fctr^6
## 0.0442295787 -0.0260159873
## Q100562.fctrNo Q100689.fctrYes
## -0.0494254458 -0.0180709793
## Q101163.fctrDad Q102089.fctrOwn
## 0.0843263803 0.0144604979
## Q104996.fctrYes Q105655.fctrYes
## -0.0421336716 0.0537495668
## Q106272.fctrNo Q106272.fctrYes
## -0.0440962950 0.0061537156
## Q106388.fctrYes Q106997.fctrGr
## 0.0118220695 0.0908393370
## Q106997.fctrYy Q108855.fctrUmm...
## -0.0036855537 -0.0346197217
## Q108855.fctrYes! Q109367.fctrNo
## 0.0020635074 0.0412091249
## Q109367.fctrYes Q110740.fctrPC
## -0.0121587305 0.0337873914
## Q112512.fctrNo Q113181.fctrNo
## -0.0077184208 -0.1304684021
## Q113181.fctrYes Q114386.fctrMysterious
## 0.0415001715 -0.0297775619
## Q114517.fctrNo Q115390.fctrNo
## -0.0494363509 0.0057597672
## Q115611.fctrNo Q115611.fctrYes
## -0.0419695889 0.1456303371
## Q115899.fctrCs Q116197.fctrA.M.
## -0.0369977321 0.0011445068
## Q116601.fctrNo Q116881.fctrHappy
## -0.0225810498 -0.0360484418
## Q116881.fctrRight Q116953.fctrNo
## 0.0898487476 0.0266948858
## Q117193.fctrStandard hours Q118233.fctrYes
## 0.0230113403 -0.0002013763
## Q118892.fctrNo Q119650.fctrGiving
## -0.0424150852 0.0610822146
## Q120194.fctrStudy first Q120379.fctrYes
## -0.0055384870 -0.0216543760
## Q120472.fctrScience Q122120.fctrYes
## 0.0422311228 0.0034867935
## Q122769.fctrYes Q122771.fctrPt
## 0.0155657944 0.0064870270
## Q123464.fctrYes Q123621.fctrYes
## 0.0060694262 0.0370626403
## Q124742.fctrNo Q96024.fctrNo
## -0.0574081038 -0.0062556082
## Q98059.fctrYes Q98197.fctrNo
## -0.0003727267 -0.0849754591
## Q98869.fctrNo Q99480.fctrNo
## -0.0731334996 -0.0747086313
## Q99480.fctrYes Q99716.fctrYes
## 0.0105675685 -0.0265170703
## Hhold.fctrPKy:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr2
## -0.0001313043 0.0384869213
## Hhold.fctrSKy:.clusterid.fctr3 Hhold.fctrN:.clusterid.fctr4
## -0.0242796402 0.0278282151
## Hhold.fctrMKy:.clusterid.fctr4 Hhold.fctrPKy:.clusterid.fctr4
## 0.0242836273 0.0411585707
## YOB.Age.fctr(25,30]:YOB.Age.dff YOB.Age.fctr(35,40]:YOB.Age.dff
## 0.0239437711 -0.0390044204
## [1] "myfit_mdl: train diagnostics complete: 18.367000 secs"
## Prediction
## Reference D R
## D 461 363
## R 295 838
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.637711e-01 3.024994e-01 6.423517e-01 6.846987e-01 5.789474e-01
## AccuracyPValue McnemarPValue
## 8.843720e-15 9.003218e-03
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy
## 10 0.45 0.7078652 0.5856574
## 11 0.50 0.6780186 0.5856574## Prediction
## Reference D R
## D 75 139
## R 69 219
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.856574e-01 1.157481e-01 5.411697e-01 6.291304e-01 5.737052e-01
## AccuracyPValue McnemarPValue
## 3.104270e-01 1.715935e-06
## [1] "myfit_mdl: predict complete: 28.048000 secs"
## id
## 1 All.X#center.scale#rcv#glmnet
## feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 16.964 1.664
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.621966 0.3992718 0.8446602 0.699893
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.55 0.7180805 0.6128487
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6423517 0.6846987 0.1649007
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.555442 0.3504673 0.7604167 0.5688766
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.6780186 0.5856574
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5411697 0.6291304 0.1157481
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009997796 0.019356
## [1] "myfit_mdl: exit: 28.430000 secs"
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X#range#rcv#glmnet"
## [1] " indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.716000 secs"## Warning in preProcess.default(method = "range", x =
## structure(c(-0.480112420809766, : No variation for for:
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0333 on full training set## Warning in preProcess.default(thresh = 0.95, k = 5, method =
## "range", x = structure(c(-0.480112420809766, : No variation for
## for: Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff
## [1] "myfit_mdl: train complete: 16.252000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = bstMdlIdComponents$family, : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 85 -none- numeric
## beta 22100 dgCMatrix S4
## df 85 -none- numeric
## dim 2 -none- numeric
## lambda 85 -none- numeric
## dev.ratio 85 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 260 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.2216507808 0.1223388126
## Hhold.fctrPKn Hhold.fctrPKy
## -0.5431418661 -0.6618709561
## Hhold.fctrSKy Income.fctr.Q
## -0.1090177325 0.0962696997
## Income.fctr.C Income.fctr^6
## 0.0824986160 -0.0643118203
## Q100562.fctrNo Q100689.fctrYes
## -0.1227211571 -0.0242725337
## Q101163.fctrDad Q102089.fctrOwn
## 0.1609136964 0.0213110713
## Q104996.fctrYes Q105655.fctrYes
## -0.0733859891 0.0994923204
## Q106272.fctrNo Q106272.fctrYes
## -0.0985282958 0.0109728198
## Q106388.fctrYes Q106997.fctrGr
## 0.0222597292 0.1722153193
## Q106997.fctrYy Q108855.fctrUmm...
## -0.0005482815 -0.0640940960
## Q108855.fctrYes! Q109367.fctrNo
## 0.0043637645 0.0776512027
## Q109367.fctrYes Q110740.fctrPC
## -0.0232319681 0.0617129947
## Q112512.fctrNo Q113181.fctrNo
## -0.0089942731 -0.2565348387
## Q113181.fctrYes Q114386.fctrMysterious
## 0.0883115008 -0.0488277360
## Q114517.fctrNo Q115390.fctrNo
## -0.0891692654 0.0037303385
## Q115611.fctrNo Q115611.fctrYes
## -0.0867549557 0.2945956632
## Q115899.fctrCs Q116601.fctrNo
## -0.0700586145 -0.0530411889
## Q116881.fctrHappy Q116881.fctrRight
## -0.0660674927 0.2077280095
## Q116953.fctrNo Q117193.fctrStandard hours
## 0.0531229393 0.0358169094
## Q118892.fctrNo Q119650.fctrGiving
## -0.0768159368 0.1151091086
## Q120194.fctrStudy first Q120379.fctrYes
## -0.0034275168 -0.0337302239
## Q120472.fctrScience Q122769.fctrYes
## 0.0750873634 0.0270370204
## Q122771.fctrPt Q123464.fctrYes
## 0.0093972520 0.0018784552
## Q123621.fctrYes Q124742.fctrNo
## 0.0702491183 -0.1091270840
## Q96024.fctrNo Q98197.fctrNo
## -0.0097694988 -0.1666944739
## Q98869.fctrNo Q99480.fctrNo
## -0.1988235708 -0.1985567368
## Q99480.fctrYes Q99716.fctrYes
## 0.0163095000 -0.0848727311
## Hhold.fctrSKn:.clusterid.fctr2 Hhold.fctrSKy:.clusterid.fctr3
## 0.0972361315 -0.2486718925
## Hhold.fctrN:.clusterid.fctr4 Hhold.fctrMKy:.clusterid.fctr4
## 0.2316323732 0.0799692788
## Hhold.fctrPKy:.clusterid.fctr4 YOB.Age.fctr(25,30]:YOB.Age.dff
## 1.0850351271 0.0874531579
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.1693273916
## [1] "max lambda < lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.2181242685 0.1268980348
## Hhold.fctrPKn Hhold.fctrPKy
## -0.5667951509 -0.7360179842
## Hhold.fctrSKy Income.fctr.Q
## -0.1223613415 0.1056876292
## Income.fctr.C Income.fctr^6
## 0.0964955979 -0.0780271437
## Q100562.fctrNo Q100689.fctrYes
## -0.1336075822 -0.0361502891
## Q101163.fctrDad Q102089.fctrOwn
## 0.1686107441 0.0295085694
## Q104996.fctrYes Q105655.fctrYes
## -0.0849702314 0.1075000884
## Q106272.fctrNo Q106272.fctrYes
## -0.1055147182 0.0127575171
## Q106388.fctrYes Q106997.fctrGr
## 0.0276876095 0.1820281443
## Q106997.fctrYy Q108855.fctrUmm...
## -0.0075273343 -0.0728898757
## Q108855.fctrYes! Q109367.fctrNo
## 0.0041288338 0.0844117618
## Q109367.fctrYes Q110740.fctrPC
## -0.0244431062 0.0676957492
## Q112512.fctrNo Q113181.fctrNo
## -0.0209172822 -0.2616295419
## Q113181.fctrYes Q114386.fctrMysterious
## 0.0851082794 -0.0595585781
## Q114517.fctrNo Q115390.fctrNo
## -0.1000702347 0.0122548916
## Q115611.fctrNo Q115611.fctrYes
## -0.0839508900 0.3033680414
## Q115899.fctrCs Q116197.fctrA.M.
## -0.0787815553 0.0025657763
## Q116601.fctrNo Q116881.fctrHappy
## -0.0643774543 -0.0725092183
## Q116881.fctrRight Q116953.fctrNo
## 0.2118371133 0.0609143935
## Q117193.fctrStandard hours Q118233.fctrYes
## 0.0462755734 -0.0004803217
## Q118892.fctrNo Q119650.fctrGiving
## -0.0889319734 0.1258648670
## Q120194.fctrStudy first Q120379.fctrYes
## -0.0111318453 -0.0460010967
## Q120472.fctrScience Q122120.fctrYes
## 0.0857212012 0.0087862652
## Q122769.fctrYes Q122771.fctrPt
## 0.0340947285 0.0181767014
## Q123464.fctrYes Q123621.fctrYes
## 0.0299377349 0.0757128321
## Q124742.fctrNo Q96024.fctrNo
## -0.1201268773 -0.0136151428
## Q98059.fctrYes Q98197.fctrNo
## -0.0008977055 -0.1704429347
## Q98869.fctrNo Q99480.fctrNo
## -0.2057924299 -0.2024638922
## Q99480.fctrYes Q99716.fctrYes
## 0.0226409850 -0.0981662351
## Hhold.fctrPKy:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr2
## -0.0058086382 0.1098872900
## Hhold.fctrSKy:.clusterid.fctr3 Hhold.fctrN:.clusterid.fctr4
## -0.2880193501 0.2766211491
## Hhold.fctrMKy:.clusterid.fctr4 Hhold.fctrPKy:.clusterid.fctr4
## 0.0932839092 1.2878098547
## YOB.Age.fctr(25,30]:YOB.Age.dff YOB.Age.fctr(35,40]:YOB.Age.dff
## 0.1084505466 -0.1907975377
## [1] "myfit_mdl: train diagnostics complete: 16.922000 secs"
## Prediction
## Reference D R
## D 461 363
## R 295 838
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.637711e-01 3.024994e-01 6.423517e-01 6.846987e-01 5.789474e-01
## AccuracyPValue McnemarPValue
## 8.843720e-15 9.003218e-03
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy
## 10 0.45 0.7078652 0.5856574
## 11 0.50 0.6780186 0.5856574
## Prediction
## Reference D R
## D 75 139
## R 69 219
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.856574e-01 1.157481e-01 5.411697e-01 6.291304e-01 5.737052e-01
## AccuracyPValue McnemarPValue
## 3.104270e-01 1.715935e-06
## [1] "myfit_mdl: predict complete: 26.550000 secs"
## id
## 1 All.X#range#rcv#glmnet
## feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 15.454 1.571
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.621966 0.3992718 0.8446602 0.699893
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.55 0.7180805 0.6128487
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6423517 0.6846987 0.1649007
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.555442 0.3504673 0.7604167 0.5688766
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.6780186 0.5856574
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5411697 0.6291304 0.1157481
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009997796 0.019356
## [1] "myfit_mdl: exit: 26.930000 secs"
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X#zv.pca#rcv#glmnet"
## [1] " indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.673000 secs"
## + Fold1.Rep1: alpha=0.100, lambda=0.02429
## - Fold1.Rep1: alpha=0.100, lambda=0.02429
## + Fold1.Rep1: alpha=0.325, lambda=0.02429
## - Fold1.Rep1: alpha=0.325, lambda=0.02429
## + Fold1.Rep1: alpha=0.550, lambda=0.02429
## - Fold1.Rep1: alpha=0.550, lambda=0.02429
## + Fold1.Rep1: alpha=0.775, lambda=0.02429
## - Fold1.Rep1: alpha=0.775, lambda=0.02429
## + Fold1.Rep1: alpha=1.000, lambda=0.02429
## - Fold1.Rep1: alpha=1.000, lambda=0.02429
## + Fold2.Rep1: alpha=0.100, lambda=0.02429
## - Fold2.Rep1: alpha=0.100, lambda=0.02429
## + Fold2.Rep1: alpha=0.325, lambda=0.02429
## - Fold2.Rep1: alpha=0.325, lambda=0.02429
## + Fold2.Rep1: alpha=0.550, lambda=0.02429
## - Fold2.Rep1: alpha=0.550, lambda=0.02429
## + Fold2.Rep1: alpha=0.775, lambda=0.02429
## - Fold2.Rep1: alpha=0.775, lambda=0.02429
## + Fold2.Rep1: alpha=1.000, lambda=0.02429
## - Fold2.Rep1: alpha=1.000, lambda=0.02429
## + Fold3.Rep1: alpha=0.100, lambda=0.02429
## - Fold3.Rep1: alpha=0.100, lambda=0.02429
## + Fold3.Rep1: alpha=0.325, lambda=0.02429
## - Fold3.Rep1: alpha=0.325, lambda=0.02429
## + Fold3.Rep1: alpha=0.550, lambda=0.02429
## - Fold3.Rep1: alpha=0.550, lambda=0.02429
## + Fold3.Rep1: alpha=0.775, lambda=0.02429
## - Fold3.Rep1: alpha=0.775, lambda=0.02429
## + Fold3.Rep1: alpha=1.000, lambda=0.02429
## - Fold3.Rep1: alpha=1.000, lambda=0.02429
## + Fold1.Rep2: alpha=0.100, lambda=0.02429
## - Fold1.Rep2: alpha=0.100, lambda=0.02429
## + Fold1.Rep2: alpha=0.325, lambda=0.02429
## - Fold1.Rep2: alpha=0.325, lambda=0.02429
## + Fold1.Rep2: alpha=0.550, lambda=0.02429
## - Fold1.Rep2: alpha=0.550, lambda=0.02429
## + Fold1.Rep2: alpha=0.775, lambda=0.02429
## - Fold1.Rep2: alpha=0.775, lambda=0.02429
## + Fold1.Rep2: alpha=1.000, lambda=0.02429
## - Fold1.Rep2: alpha=1.000, lambda=0.02429
## + Fold2.Rep2: alpha=0.100, lambda=0.02429
## - Fold2.Rep2: alpha=0.100, lambda=0.02429
## + Fold2.Rep2: alpha=0.325, lambda=0.02429
## - Fold2.Rep2: alpha=0.325, lambda=0.02429
## + Fold2.Rep2: alpha=0.550, lambda=0.02429
## - Fold2.Rep2: alpha=0.550, lambda=0.02429
## + Fold2.Rep2: alpha=0.775, lambda=0.02429
## - Fold2.Rep2: alpha=0.775, lambda=0.02429
## + Fold2.Rep2: alpha=1.000, lambda=0.02429
## - Fold2.Rep2: alpha=1.000, lambda=0.02429
## + Fold3.Rep2: alpha=0.100, lambda=0.02429
## - Fold3.Rep2: alpha=0.100, lambda=0.02429
## + Fold3.Rep2: alpha=0.325, lambda=0.02429
## - Fold3.Rep2: alpha=0.325, lambda=0.02429
## + Fold3.Rep2: alpha=0.550, lambda=0.02429
## - Fold3.Rep2: alpha=0.550, lambda=0.02429
## + Fold3.Rep2: alpha=0.775, lambda=0.02429
## - Fold3.Rep2: alpha=0.775, lambda=0.02429
## + Fold3.Rep2: alpha=1.000, lambda=0.02429
## - Fold3.Rep2: alpha=1.000, lambda=0.02429
## + Fold1.Rep3: alpha=0.100, lambda=0.02429
## - Fold1.Rep3: alpha=0.100, lambda=0.02429
## + Fold1.Rep3: alpha=0.325, lambda=0.02429
## - Fold1.Rep3: alpha=0.325, lambda=0.02429
## + Fold1.Rep3: alpha=0.550, lambda=0.02429
## - Fold1.Rep3: alpha=0.550, lambda=0.02429
## + Fold1.Rep3: alpha=0.775, lambda=0.02429
## - Fold1.Rep3: alpha=0.775, lambda=0.02429
## + Fold1.Rep3: alpha=1.000, lambda=0.02429
## - Fold1.Rep3: alpha=1.000, lambda=0.02429
## + Fold2.Rep3: alpha=0.100, lambda=0.02429
## - Fold2.Rep3: alpha=0.100, lambda=0.02429
## + Fold2.Rep3: alpha=0.325, lambda=0.02429
## - Fold2.Rep3: alpha=0.325, lambda=0.02429
## + Fold2.Rep3: alpha=0.550, lambda=0.02429
## - Fold2.Rep3: alpha=0.550, lambda=0.02429
## + Fold2.Rep3: alpha=0.775, lambda=0.02429
## - Fold2.Rep3: alpha=0.775, lambda=0.02429
## + Fold2.Rep3: alpha=1.000, lambda=0.02429
## - Fold2.Rep3: alpha=1.000, lambda=0.02429
## + Fold3.Rep3: alpha=0.100, lambda=0.02429
## - Fold3.Rep3: alpha=0.100, lambda=0.02429
## + Fold3.Rep3: alpha=0.325, lambda=0.02429
## - Fold3.Rep3: alpha=0.325, lambda=0.02429
## + Fold3.Rep3: alpha=0.550, lambda=0.02429
## - Fold3.Rep3: alpha=0.550, lambda=0.02429
## + Fold3.Rep3: alpha=0.775, lambda=0.02429
## - Fold3.Rep3: alpha=0.775, lambda=0.02429
## + Fold3.Rep3: alpha=1.000, lambda=0.02429
## - Fold3.Rep3: alpha=1.000, lambda=0.02429
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.0243 on full training set
## [1] "myfit_mdl: train complete: 42.876000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = bstMdlIdComponents$family, : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 65 -none- numeric
## beta 9490 dgCMatrix S4
## df 65 -none- numeric
## dim 2 -none- numeric
## lambda 65 -none- numeric
## dev.ratio 65 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 146 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) PC2 PC3 PC4 PC7
## 3.236019e-01 -2.823328e-02 5.546607e-02 1.115289e-05 5.453458e-02
## PC9 PC14 PC15 PC17 PC27
## -6.619760e-02 -5.940572e-02 -5.724565e-03 2.017187e-03 -5.410136e-03
## PC30 PC38 PC42 PC48 PC84
## -1.392356e-02 4.973894e-03 -8.691699e-03 -2.897975e-02 1.764252e-02
## PC101 PC114 PC118 PC120 PC121
## 3.794732e-03 -6.291254e-03 8.088300e-03 -1.026525e-02 -4.096242e-02
## PC127
## -2.685008e-02
## [1] "max lambda < lambdaOpt:"
## (Intercept) PC2 PC3 PC4 PC7
## 0.3245533430 -0.0306746910 0.0585083635 0.0031999884 0.0585529335
## PC9 PC14 PC15 PC17 PC22
## -0.0704141605 -0.0642735896 -0.0104591053 0.0067864724 0.0030560393
## PC24 PC27 PC29 PC30 PC38
## -0.0045827626 -0.0106044837 -0.0049510694 -0.0191420782 0.0104629699
## PC42 PC48 PC82 PC84 PC95
## -0.0143327180 -0.0347440299 0.0003084852 0.0243742872 0.0018522015
## PC101 PC114 PC118 PC120 PC121
## 0.0110442394 -0.0140695404 0.0160689186 -0.0183766924 -0.0491504946
## PC127
## -0.0354056112
## [1] "myfit_mdl: train diagnostics complete: 43.509000 secs"
## Prediction
## Reference D R
## D 367 457
## R 277 856
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.249361e-01 2.070730e-01 6.030565e-01 6.464407e-01 5.789474e-01
## AccuracyPValue McnemarPValue
## 1.895803e-05 3.921581e-11
## Prediction
## Reference D R
## D 92 122
## R 89 199
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5796813 0.1233219 0.5351418 0.6232769 0.5737052
## AccuracyPValue McnemarPValue
## 0.4115799 0.0275968
## [1] "myfit_mdl: predict complete: 53.219000 secs"
## id
## 1 All.X#zv.pca#rcv#glmnet
## feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 42.121 1.133
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5597418 0.2050971 0.9143866 0.6546639
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.55 0.6999182 0.595983
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6030565 0.6464407 0.09782945
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5017848 0.135514 0.8680556 0.5588331
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.55 0.6535304 0.5796813
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5351418 0.6232769 0.1233219
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008857455 0.0194164
## [1] "myfit_mdl: exit: 53.655000 secs"
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X#ica#rcv#glmnet"
## [1] " indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = bstMdlIdComponents$family, : myfit_mdl: preProcess
## method: range currently does not work for columns with no variance:
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## [1] "myfit_mdl: setup complete: 0.810000 secs"## Warning in preProcess.default(method = "ica", n.comp = 3, x =
## structure(c(-0.480112420809766, : These variables have zero variances:
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## + Fold1.Rep1: alpha=0.100, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold1.Rep1: alpha=0.100, lambda=0.02503
## + Fold1.Rep1: alpha=0.325, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold1.Rep1: alpha=0.325, lambda=0.02503
## + Fold1.Rep1: alpha=0.550, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold1.Rep1: alpha=0.550, lambda=0.02503
## + Fold1.Rep1: alpha=0.775, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold1.Rep1: alpha=0.775, lambda=0.02503
## + Fold1.Rep1: alpha=1.000, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold1.Rep1: alpha=1.000, lambda=0.02503
## + Fold2.Rep1: alpha=0.100, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr2,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrPKy:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold2.Rep1: alpha=0.100, lambda=0.02503
## + Fold2.Rep1: alpha=0.325, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr2,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrPKy:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold2.Rep1: alpha=0.325, lambda=0.02503
## + Fold2.Rep1: alpha=0.550, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr2,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrPKy:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold2.Rep1: alpha=0.550, lambda=0.02503
## + Fold2.Rep1: alpha=0.775, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr2,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrPKy:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold2.Rep1: alpha=0.775, lambda=0.02503
## + Fold2.Rep1: alpha=1.000, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr2,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrPKy:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold2.Rep1: alpha=1.000, lambda=0.02503
## + Fold3.Rep1: alpha=0.100, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr3,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold3.Rep1: alpha=0.100, lambda=0.02503
## + Fold3.Rep1: alpha=0.325, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr3,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold3.Rep1: alpha=0.325, lambda=0.02503
## + Fold3.Rep1: alpha=0.550, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr3,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold3.Rep1: alpha=0.550, lambda=0.02503
## + Fold3.Rep1: alpha=0.775, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr3,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold3.Rep1: alpha=0.775, lambda=0.02503
## + Fold3.Rep1: alpha=1.000, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr3,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold3.Rep1: alpha=1.000, lambda=0.02503
## + Fold1.Rep2: alpha=0.100, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold1.Rep2: alpha=0.100, lambda=0.02503
## + Fold1.Rep2: alpha=0.325, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold1.Rep2: alpha=0.325, lambda=0.02503
## + Fold1.Rep2: alpha=0.550, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold1.Rep2: alpha=0.550, lambda=0.02503
## + Fold1.Rep2: alpha=0.775, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold1.Rep2: alpha=0.775, lambda=0.02503
## + Fold1.Rep2: alpha=1.000, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold1.Rep2: alpha=1.000, lambda=0.02503
## + Fold2.Rep2: alpha=0.100, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr3,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold2.Rep2: alpha=0.100, lambda=0.02503
## + Fold2.Rep2: alpha=0.325, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr3,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold2.Rep2: alpha=0.325, lambda=0.02503
## + Fold2.Rep2: alpha=0.550, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr3,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold2.Rep2: alpha=0.550, lambda=0.02503
## + Fold2.Rep2: alpha=0.775, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr3,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold2.Rep2: alpha=0.775, lambda=0.02503
## + Fold2.Rep2: alpha=1.000, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr3,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold2.Rep2: alpha=1.000, lambda=0.02503
## + Fold3.Rep2: alpha=0.100, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr2,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold3.Rep2: alpha=0.100, lambda=0.02503
## + Fold3.Rep2: alpha=0.325, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr2,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold3.Rep2: alpha=0.325, lambda=0.02503
## + Fold3.Rep2: alpha=0.550, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr2,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold3.Rep2: alpha=0.550, lambda=0.02503
## + Fold3.Rep2: alpha=0.775, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr2,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold3.Rep2: alpha=0.775, lambda=0.02503
## + Fold3.Rep2: alpha=1.000, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr2,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold3.Rep2: alpha=1.000, lambda=0.02503
## + Fold1.Rep3: alpha=0.100, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr2,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold1.Rep3: alpha=0.100, lambda=0.02503
## + Fold1.Rep3: alpha=0.325, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr2,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold1.Rep3: alpha=0.325, lambda=0.02503
## + Fold1.Rep3: alpha=0.550, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr2,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold1.Rep3: alpha=0.550, lambda=0.02503
## + Fold1.Rep3: alpha=0.775, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr2,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold1.Rep3: alpha=0.775, lambda=0.02503
## + Fold1.Rep3: alpha=1.000, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr2,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold1.Rep3: alpha=1.000, lambda=0.02503
## + Fold2.Rep3: alpha=0.100, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold2.Rep3: alpha=0.100, lambda=0.02503
## + Fold2.Rep3: alpha=0.325, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold2.Rep3: alpha=0.325, lambda=0.02503
## + Fold2.Rep3: alpha=0.550, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold2.Rep3: alpha=0.550, lambda=0.02503
## + Fold2.Rep3: alpha=0.775, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold2.Rep3: alpha=0.775, lambda=0.02503
## + Fold2.Rep3: alpha=1.000, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff## - Fold2.Rep3: alpha=1.000, lambda=0.02503
## + Fold3.Rep3: alpha=0.100, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr3,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold3.Rep3: alpha=0.100, lambda=0.02503
## + Fold3.Rep3: alpha=0.325, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr3,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold3.Rep3: alpha=0.325, lambda=0.02503
## + Fold3.Rep3: alpha=0.550, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr3,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold3.Rep3: alpha=0.550, lambda=0.02503
## + Fold3.Rep3: alpha=0.775, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr3,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold3.Rep3: alpha=0.775, lambda=0.02503
## + Fold3.Rep3: alpha=1.000, lambda=0.02503## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKy:.clusterid.fctr3,
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## - Fold3.Rep3: alpha=1.000, lambda=0.02503
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.00539 on full training set## Warning in preProcess.default(thresh = 0.95, k = 5, method = "ica", n.comp
## = 3, : These variables have zero variances: Hhold.fctrPKn:.clusterid.fctr4,
## Hhold.fctrSKn:.clusterid.fctr4, Hhold.fctrSKy:.clusterid.fctr4,
## YOB.Age.fctrNA:YOB.Age.dff
## [1] "myfit_mdl: train complete: 23.741000 secs"
## Length Class Mode
## a0 37 -none- numeric
## beta 111 dgCMatrix S4
## df 37 -none- numeric
## dim 2 -none- numeric
## lambda 37 -none- numeric
## dev.ratio 37 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 3 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) ICA1 ICA2 ICA3
## 0.324410558 0.161623052 -0.226049140 -0.001036744
## [1] "max lambda < lambdaOpt:"
## (Intercept) ICA1 ICA2 ICA3
## 0.324501947 0.163025559 -0.227524076 -0.002238236
## [1] "myfit_mdl: train diagnostics complete: 24.306000 secs"
## Prediction
## Reference D R
## D 135 689
## R 103 1030
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.952989e-01 8.075748e-02 5.731676e-01 6.171443e-01 5.789474e-01
## AccuracyPValue McnemarPValue
## 7.442809e-02 5.666712e-96
## Prediction
## Reference D R
## D 1 213
## R 1 287
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.737052e-01 1.375772e-03 5.291204e-01 6.174170e-01 5.737052e-01
## AccuracyPValue McnemarPValue
## 5.188758e-01 3.673187e-47
## [1] "myfit_mdl: predict complete: 33.939000 secs"
## id
## 1 All.X#ica#rcv#glmnet
## feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 15 22.835 0.469
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5364629 0.163835 0.9090909 0.5849557
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.7223001 0.5918896
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.5731676 0.6171443 0.07196172
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.4820223 0.1168224 0.8472222 0.5297573
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7284264 0.5737052
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5291204 0.617417 0.001375772
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008635588 0.0194654
## [1] "myfit_mdl: exit: 34.332000 secs"
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X#spatialSign#rcv#glmnet"
## [1] " indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.692000 secs"## Warning in preProcess.default(method = "spatialSign", x =
## structure(c(-0.480112420809766, : These variables have zero variances:
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0335 on full training set## Warning in preProcess.default(thresh = 0.95, k = 5, method
## = "spatialSign", : These variables have zero variances:
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff
## [1] "myfit_mdl: train complete: 21.317000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = bstMdlIdComponents$family, : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 84 -none- numeric
## beta 21840 dgCMatrix S4
## df 84 -none- numeric
## dim 2 -none- numeric
## lambda 84 -none- numeric
## dev.ratio 84 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 260 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.32741793 0.75040355
## Hhold.fctrPKn Hhold.fctrPKy
## -1.68803724 -1.24808973
## Hhold.fctrSKy Income.fctr.Q
## -0.39315414 0.50164688
## Income.fctr.C Income.fctr^6
## 0.68731976 -0.41910686
## Q100562.fctrNo Q100689.fctrYes
## -0.60162473 -0.10679590
## Q101163.fctrDad Q102089.fctrOwn
## 1.24090131 0.23282600
## Q104996.fctrYes Q105655.fctrYes
## -0.46673055 0.74393721
## Q106272.fctrNo Q106272.fctrYes
## -0.71986043 0.02087179
## Q106388.fctrYes Q106997.fctrGr
## 0.02035863 1.33529126
## Q106997.fctrYy Q108855.fctrUmm...
## -0.03427569 -0.41928096
## Q108855.fctrYes! Q109367.fctrNo
## 0.06592898 0.56344697
## Q109367.fctrYes Q110740.fctrPC
## -0.20909832 0.42052728
## Q112512.fctrNo Q113181.fctrNo
## -0.05234640 -2.08828558
## Q113181.fctrYes Q114386.fctrMysterious
## 0.72459505 -0.33142096
## Q114517.fctrNo Q115390.fctrNo
## -0.73064059 0.04160894
## Q115611.fctrNo Q115611.fctrYes
## -0.67735622 2.24911631
## Q115899.fctrCs Q116601.fctrNo
## -0.46697668 -0.26072188
## Q116881.fctrHappy Q116881.fctrRight
## -0.46152051 1.40387104
## Q116953.fctrNo Q117193.fctrStandard hours
## 0.39505452 0.23335091
## Q118892.fctrNo Q119650.fctrGiving
## -0.54623480 0.89295237
## Q120194.fctrStudy first Q120379.fctrYes
## -0.01261912 -0.26652882
## Q120472.fctrScience Q122769.fctrYes
## 0.53767804 0.18597159
## Q122771.fctrPt Q123464.fctrYes
## 0.08719323 0.04157432
## Q123621.fctrYes Q124742.fctrNo
## 0.58861333 -0.88733674
## Q96024.fctrNo Q98197.fctrNo
## -0.09013602 -1.15603900
## Q98869.fctrNo Q99480.fctrNo
## -1.22932412 -1.22813909
## Q99480.fctrYes Q99716.fctrYes
## 0.03457696 -0.41488311
## Hhold.fctrPKy:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr2
## -0.15159512 0.39750663
## Hhold.fctrSKy:.clusterid.fctr3 Hhold.fctrN:.clusterid.fctr4
## -0.39921752 0.44480242
## Hhold.fctrMKy:.clusterid.fctr4 Hhold.fctrPKy:.clusterid.fctr4
## 0.40341565 0.86413578
## YOB.Age.fctr(25,30]:YOB.Age.dff YOB.Age.fctr(35,40]:YOB.Age.dff
## 0.25949987 -0.50116421
## YOB.Age.fctr(40,50]:YOB.Age.dff
## 0.01479411
## [1] "max lambda < lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.328268741 0.773064515
## Hhold.fctrPKn Hhold.fctrPKy
## -1.754199338 -1.350737217
## Hhold.fctrSKy Income.fctr.Q
## -0.441914384 0.555393842
## Income.fctr.C Income.fctr^6
## 0.788045928 -0.488125918
## Q100562.fctrNo Q100689.fctrYes
## -0.662579987 -0.202109638
## Q101163.fctrDad Q102089.fctrOwn
## 1.302141218 0.295146239
## Q104996.fctrYes Q105655.fctrYes
## -0.559569453 0.811109328
## Q106042.fctrYes Q106272.fctrNo
## -0.011659886 -0.765322248
## Q106272.fctrYes Q106388.fctrYes
## 0.029703741 0.053843276
## Q106997.fctrGr Q106997.fctrYy
## 1.417343736 -0.084644905
## Q108855.fctrUmm... Q108855.fctrYes!
## -0.484733387 0.070467853
## Q109367.fctrNo Q109367.fctrYes
## 0.613756577 -0.223817695
## Q110740.fctrPC Q111220.fctrYes
## 0.471033230 -0.010983636
## Q112512.fctrNo Q113181.fctrNo
## -0.120100015 -2.124905356
## Q113181.fctrYes Q114386.fctrMysterious
## 0.707447643 -0.415622365
## Q114517.fctrNo Q115390.fctrNo
## -0.815549766 0.105571424
## Q115611.fctrNo Q115611.fctrYes
## -0.650541496 2.319240433
## Q115777.fctrEnd Q115899.fctrCs
## 0.007412728 -0.525282462
## Q116601.fctrNo Q116881.fctrHappy
## -0.325008618 -0.512633753
## Q116881.fctrRight Q116953.fctrNo
## 1.430922885 0.452515847
## Q117193.fctrOdd hours Q117193.fctrStandard hours
## -0.010881333 0.305239616
## Q118892.fctrNo Q119650.fctrGiving
## -0.638869856 0.974839975
## Q120194.fctrStudy first Q120379.fctrYes
## -0.073269669 -0.355098823
## Q120472.fctrScience Q122120.fctrYes
## 0.618395258 0.026313287
## Q122769.fctrYes Q122771.fctrPt
## 0.239529477 0.139789989
## Q123464.fctrYes Q123621.fctrYes
## 0.138830158 0.630236393
## Q124742.fctrNo Q96024.fctrNo
## -0.970287555 -0.117362646
## Q98197.fctrNo Q98869.fctrNo
## -1.183603057 -1.269889599
## Q99480.fctrNo Q99480.fctrYes
## -1.250853132 0.079564926
## Q99716.fctrYes Hhold.fctrPKn:.clusterid.fctr2
## -0.476464808 -0.011937211
## Hhold.fctrPKy:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr2
## -0.306251542 0.465752126
## Hhold.fctrPKy:.clusterid.fctr3 Hhold.fctrSKy:.clusterid.fctr3
## -0.112831248 -0.463706172
## Hhold.fctrN:.clusterid.fctr4 Hhold.fctrMKy:.clusterid.fctr4
## 0.528769413 0.458820976
## Hhold.fctrPKy:.clusterid.fctr4 YOB.Age.fctr(25,30]:YOB.Age.dff
## 1.052043670 0.338048742
## YOB.Age.fctr(35,40]:YOB.Age.dff YOB.Age.fctr(40,50]:YOB.Age.dff
## -0.558975675 0.069307316
## [1] "myfit_mdl: train diagnostics complete: 21.961000 secs"
## Prediction
## Reference D R
## D 459 365
## R 296 837
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.622381e-01 2.992021e-01 6.407976e-01 6.831914e-01 5.789474e-01
## AccuracyPValue McnemarPValue
## 2.627869e-14 8.171664e-03
## Prediction
## Reference D R
## D 45 169
## R 35 253
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.936255e-01 9.651692e-02 5.492170e-01 6.369249e-01 5.737052e-01
## AccuracyPValue McnemarPValue
## 1.957903e-01 1.256100e-20
## [1] "myfit_mdl: predict complete: 32.675000 secs"
## id
## 1 All.X#spatialSign#rcv#glmnet
## feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 20.544 1.945
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.6225728 0.4004854 0.8446602 0.6991673
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.55 0.7169165 0.6118257
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6407976 0.6831914 0.1628938
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.555442 0.3504673 0.7604167 0.5704991
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.45 0.7126761 0.5936255
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.549217 0.6369249 0.09651692
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008616434 0.01531544
## [1] "myfit_mdl: exit: 33.429000 secs"
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: All.X#conditionalX#rcv#glmnet"
## [1] " indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.723000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0333 on full training set
## [1] "myfit_mdl: train complete: 14.748000 secs"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = bstMdlIdComponents$family, : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 85 -none- numeric
## beta 22100 dgCMatrix S4
## df 85 -none- numeric
## dim 2 -none- numeric
## lambda 85 -none- numeric
## dev.ratio 85 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 260 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.2689370104 0.1223388126
## Hhold.fctrPKn Hhold.fctrPKy
## -0.5431418661 -0.6618709561
## Hhold.fctrSKy Income.fctr.Q
## -0.1090177325 0.0980362635
## Income.fctr.C Income.fctr^6
## 0.1010397569 -0.0558545740
## Q100562.fctrNo Q100689.fctrYes
## -0.1227211571 -0.0242725337
## Q101163.fctrDad Q102089.fctrOwn
## 0.1609136964 0.0213110713
## Q104996.fctrYes Q105655.fctrYes
## -0.0733859891 0.0994923204
## Q106272.fctrNo Q106272.fctrYes
## -0.0985282958 0.0109728198
## Q106388.fctrYes Q106997.fctrGr
## 0.0222597292 0.1722153193
## Q106997.fctrYy Q108855.fctrUmm...
## -0.0005482815 -0.0640940960
## Q108855.fctrYes! Q109367.fctrNo
## 0.0043637645 0.0776512027
## Q109367.fctrYes Q110740.fctrPC
## -0.0232319681 0.0617129947
## Q112512.fctrNo Q113181.fctrNo
## -0.0089942731 -0.2565348387
## Q113181.fctrYes Q114386.fctrMysterious
## 0.0883115008 -0.0488277360
## Q114517.fctrNo Q115390.fctrNo
## -0.0891692654 0.0037303385
## Q115611.fctrNo Q115611.fctrYes
## -0.0867549557 0.2945956632
## Q115899.fctrCs Q116601.fctrNo
## -0.0700586145 -0.0530411889
## Q116881.fctrHappy Q116881.fctrRight
## -0.0660674927 0.2077280095
## Q116953.fctrNo Q117193.fctrStandard hours
## 0.0531229393 0.0358169094
## Q118892.fctrNo Q119650.fctrGiving
## -0.0768159368 0.1151091086
## Q120194.fctrStudy first Q120379.fctrYes
## -0.0034275168 -0.0337302239
## Q120472.fctrScience Q122769.fctrYes
## 0.0750873634 0.0270370204
## Q122771.fctrPt Q123464.fctrYes
## 0.0093972520 0.0018784552
## Q123621.fctrYes Q124742.fctrNo
## 0.0702491183 -0.1091270840
## Q96024.fctrNo Q98197.fctrNo
## -0.0097694988 -0.1666944739
## Q98869.fctrNo Q99480.fctrNo
## -0.1988235708 -0.1985567368
## Q99480.fctrYes Q99716.fctrYes
## 0.0163095000 -0.0848727311
## Hhold.fctrSKn:.clusterid.fctr2 Hhold.fctrSKy:.clusterid.fctr3
## 0.0972361315 -0.2486718925
## Hhold.fctrN:.clusterid.fctr4 Hhold.fctrMKy:.clusterid.fctr4
## 0.2316323732 0.0799692788
## Hhold.fctrPKy:.clusterid.fctr4 YOB.Age.fctr(25,30]:YOB.Age.dff
## 1.0850351271 0.0174906316
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.0338654783
## [1] "max lambda < lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.2687574078 0.1268980348
## Hhold.fctrPKn Hhold.fctrPKy
## -0.5667951509 -0.7360179842
## Hhold.fctrSKy Income.fctr.Q
## -0.1223613415 0.1076270135
## Income.fctr.C Income.fctr^6
## 0.1181824886 -0.0677662807
## Q100562.fctrNo Q100689.fctrYes
## -0.1336075822 -0.0361502891
## Q101163.fctrDad Q102089.fctrOwn
## 0.1686107441 0.0295085694
## Q104996.fctrYes Q105655.fctrYes
## -0.0849702314 0.1075000884
## Q106272.fctrNo Q106272.fctrYes
## -0.1055147182 0.0127575171
## Q106388.fctrYes Q106997.fctrGr
## 0.0276876095 0.1820281443
## Q106997.fctrYy Q108855.fctrUmm...
## -0.0075273343 -0.0728898757
## Q108855.fctrYes! Q109367.fctrNo
## 0.0041288338 0.0844117618
## Q109367.fctrYes Q110740.fctrPC
## -0.0244431062 0.0676957492
## Q112512.fctrNo Q113181.fctrNo
## -0.0209172822 -0.2616295419
## Q113181.fctrYes Q114386.fctrMysterious
## 0.0851082794 -0.0595585781
## Q114517.fctrNo Q115390.fctrNo
## -0.1000702347 0.0122548916
## Q115611.fctrNo Q115611.fctrYes
## -0.0839508900 0.3033680414
## Q115899.fctrCs Q116197.fctrA.M.
## -0.0787815553 0.0025657763
## Q116601.fctrNo Q116881.fctrHappy
## -0.0643774543 -0.0725092183
## Q116881.fctrRight Q116953.fctrNo
## 0.2118371133 0.0609143935
## Q117193.fctrStandard hours Q118233.fctrYes
## 0.0462755734 -0.0004803217
## Q118892.fctrNo Q119650.fctrGiving
## -0.0889319734 0.1258648670
## Q120194.fctrStudy first Q120379.fctrYes
## -0.0111318453 -0.0460010967
## Q120472.fctrScience Q122120.fctrYes
## 0.0857212012 0.0087862652
## Q122769.fctrYes Q122771.fctrPt
## 0.0340947285 0.0181767014
## Q123464.fctrYes Q123621.fctrYes
## 0.0299377349 0.0757128321
## Q124742.fctrNo Q96024.fctrNo
## -0.1201268773 -0.0136151428
## Q98059.fctrYes Q98197.fctrNo
## -0.0008977055 -0.1704429347
## Q98869.fctrNo Q99480.fctrNo
## -0.2057924299 -0.2024638922
## Q99480.fctrYes Q99716.fctrYes
## 0.0226409850 -0.0981662351
## Hhold.fctrPKy:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr2
## -0.0058086382 0.1098872900
## Hhold.fctrSKy:.clusterid.fctr3 Hhold.fctrN:.clusterid.fctr4
## -0.2880193501 0.2766211491
## Hhold.fctrMKy:.clusterid.fctr4 Hhold.fctrPKy:.clusterid.fctr4
## 0.0932839092 1.2878098547
## YOB.Age.fctr(25,30]:YOB.Age.dff YOB.Age.fctr(35,40]:YOB.Age.dff
## 0.0216901093 -0.0381595075
## [1] "myfit_mdl: train diagnostics complete: 15.401000 secs"
## Prediction
## Reference D R
## D 461 363
## R 295 838
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.637711e-01 3.024994e-01 6.423517e-01 6.846987e-01 5.789474e-01
## AccuracyPValue McnemarPValue
## 8.843720e-15 9.003218e-03
## [1] "mypredict_mdl: maxMetricDf:"
## threshold f.score accuracy
## 10 0.45 0.7078652 0.5856574
## 11 0.50 0.6780186 0.5856574## Prediction
## Reference D R
## D 75 139
## R 69 219
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.856574e-01 1.157481e-01 5.411697e-01 6.291304e-01 5.737052e-01
## AccuracyPValue McnemarPValue
## 3.104270e-01 1.715935e-06
## [1] "myfit_mdl: predict complete: 24.816000 secs"
## id
## 1 All.X#conditionalX#rcv#glmnet
## feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 13.941 1.354
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.621966 0.3992718 0.8446602 0.699893
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.55 0.7180805 0.6128487
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6423517 0.6846987 0.1649007
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.555442 0.3504673 0.7604167 0.5688766
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.6780186 0.5856574
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5411697 0.6291304 0.1157481
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009997796 0.019356
## [1] "myfit_mdl: exit: 25.098000 secs"
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X#expoTrans.spatialSign#rcv#glmnet"
## [1] " indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.675000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.04 on full training set## Warning in preProcess.default(thresh = 0.95, k = 5, method
## = c("expoTrans", : These variables have zero variances:
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff
## [1] "myfit_mdl: train complete: 43.669000 secs"
## Length Class Mode
## a0 79 -none- numeric
## beta 20540 dgCMatrix S4
## df 79 -none- numeric
## dim 2 -none- numeric
## lambda 79 -none- numeric
## dev.ratio 79 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 260 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.32563670 0.67417135
## Hhold.fctrPKn Hhold.fctrPKy
## -1.51765112 -1.00926836
## Hhold.fctrSKy Income.fctr.Q
## -0.29900806 0.38478874
## Income.fctr.C Income.fctr^6
## 0.47744021 -0.28911304
## Q100562.fctrNo Q101163.fctrDad
## -0.44978648 1.10959757
## Q102089.fctrOwn Q104996.fctrYes
## 0.07119354 -0.26171798
## Q105655.fctrYes Q106272.fctrNo
## 0.58098456 -0.59728552
## Q106272.fctrYes Q106997.fctrGr
## 0.02000289 1.11200127
## Q108855.fctrUmm... Q108855.fctrYes!
## -0.28416422 0.02091655
## Q109367.fctrNo Q109367.fctrYes
## 0.44364914 -0.18111647
## Q110740.fctrPC Q113181.fctrNo
## 0.33461099 -2.01207020
## Q113181.fctrYes Q114386.fctrMysterious
## 0.79339863 -0.14367961
## Q114517.fctrNo Q115611.fctrNo
## -0.53781586 -0.72649007
## Q115611.fctrYes Q115899.fctrCs
## 2.10532294 -0.36322910
## Q116601.fctrNo Q116881.fctrHappy
## -0.10759388 -0.34200268
## Q116881.fctrRight Q116953.fctrNo
## 1.34181082 0.24794394
## Q117193.fctrStandard hours Q118892.fctrNo
## 0.05548088 -0.33766572
## Q119650.fctrGiving Q120379.fctrYes
## 0.74055045 -0.09487587
## Q120472.fctrScience Q122769.fctrYes
## 0.38568585 0.06424238
## Q123621.fctrYes Q124742.fctrNo
## 0.50618397 -0.70047580
## Q96024.fctrNo Q98197.fctrNo
## -0.01601521 -1.08212986
## Q98869.fctrNo Q99480.fctrNo
## -1.13227248 -1.15181799
## Q99716.fctrYes Hhold.fctrSKn:.clusterid.fctr2
## -0.25270143 0.22463433
## Hhold.fctrSKy:.clusterid.fctr3 Hhold.fctrN:.clusterid.fctr4
## -0.28051155 0.26037067
## Hhold.fctrMKy:.clusterid.fctr4 Hhold.fctrPKy:.clusterid.fctr4
## 0.26644698 0.46446870
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.30000777
## [1] "max lambda < lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.32648800 0.69999907
## Hhold.fctrPKn Hhold.fctrPKy
## -1.60262343 -1.14119200
## Hhold.fctrSKy Income.fctr.Q
## -0.34796051 0.44855063
## Income.fctr.C Income.fctr^6
## 0.59138435 -0.36089523
## Q100562.fctrNo Q100689.fctrYes
## -0.52448286 -0.01610129
## Q101163.fctrDad Q102089.fctrOwn
## 1.17485401 0.13769817
## Q104996.fctrYes Q105655.fctrYes
## -0.35950450 0.65713990
## Q106272.fctrNo Q106272.fctrYes
## -0.65886452 0.01767484
## Q106997.fctrGr Q108855.fctrUmm...
## 1.25290537 -0.35469685
## Q108855.fctrYes! Q109367.fctrNo
## 0.02948537 0.49864382
## Q109367.fctrYes Q110740.fctrPC
## -0.20870630 0.38193988
## Q113181.fctrNo Q113181.fctrYes
## -2.06595489 0.76394504
## Q114386.fctrMysterious Q114517.fctrNo
## -0.23375401 -0.63310915
## Q115611.fctrNo Q115611.fctrYes
## -0.69706019 2.18403976
## Q115899.fctrCs Q116601.fctrNo
## -0.42090548 -0.17740915
## Q116881.fctrHappy Q116881.fctrRight
## -0.41194390 1.36997009
## Q116953.fctrNo Q117193.fctrStandard hours
## 0.32079330 0.14279251
## Q118892.fctrNo Q119650.fctrGiving
## -0.43764433 0.82189549
## Q120379.fctrYes Q120472.fctrScience
## -0.19043021 0.47147017
## Q122769.fctrYes Q122771.fctrPt
## 0.12593502 0.04493257
## Q123621.fctrYes Q124742.fctrNo
## 0.55404281 -0.79555372
## Q96024.fctrNo Q98197.fctrNo
## -0.05396074 -1.11176387
## Q98869.fctrNo Q99480.fctrNo
## -1.18029542 -1.20084890
## Q99480.fctrYes Q99716.fctrYes
## 0.01670644 -0.32158572
## Hhold.fctrSKn:.clusterid.fctr2 Hhold.fctrSKy:.clusterid.fctr3
## 0.30456203 -0.35244350
## Hhold.fctrN:.clusterid.fctr4 Hhold.fctrMKy:.clusterid.fctr4
## 0.35723280 0.32642053
## Hhold.fctrPKy:.clusterid.fctr4 YOB.Age.fctr(25,30]:YOB.Age.dff
## 0.67927816 0.06154175
## YOB.Age.fctr(35,40]:YOB.Age.dff YOB.Age.fctr(65,90]:YOB.Age.dff
## -0.37803019 -0.01726271
## [1] "myfit_mdl: train diagnostics complete: 44.326000 secs"
## Prediction
## Reference D R
## D 440 384
## R 300 833
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.504854e-01 2.729917e-01 6.288914e-01 6.716276e-01 5.789474e-01
## AccuracyPValue McnemarPValue
## 5.693857e-11 1.505692e-03
## Prediction
## Reference D R
## D 37 177
## R 27 261
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.936255e-01 8.697978e-02 5.492170e-01 6.369249e-01 5.737052e-01
## AccuracyPValue McnemarPValue
## 1.957903e-01 1.769589e-25
## [1] "myfit_mdl: predict complete: 54.789000 secs"
## id
## 1 All.X#expoTrans.spatialSign#rcv#glmnet
## feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 42.917 2.967
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.612147 0.3725728 0.8517211 0.6915987
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.55 0.7089362 0.6128471
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6288914 0.6716276 0.160508
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.56005 0.3457944 0.7743056 0.5756912
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.45 0.7190083 0.5936255
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.549217 0.6369249 0.08697978
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009498259 0.02294536
## [1] "myfit_mdl: exit: 55.584000 secs"## min.elapsedtime.everything
## Random###myrandom_classfr 0.272
## MFO###myMFO_classfr 0.501
## Max.cor.Y.rcv.1X1###glmnet 0.766
## Max.cor.Y##rcv#rpart 1.468
## All.X##rcv#glmnet 13.519
## All.X#conditionalX#rcv#glmnet 13.941
## Low.cor.X##rcv#glmnet 14.590
## All.X#scale#rcv#glmnet 14.819
## All.X#zv#rcv#glmnet 14.938
## All.X#range#rcv#glmnet 15.454
## All.X#center#rcv#glmnet 15.479
## All.X#center.scale#rcv#glmnet 16.964
## All.X#BoxCox#rcv#glmnet 17.604
## All.X#nzv#rcv#glmnet 17.988
## All.X#spatialSign#rcv#glmnet 20.544
## All.X#ica#rcv#glmnet 22.835
## All.X#zv.pca#rcv#glmnet 42.121
## All.X#expoTrans.spatialSign#rcv#glmnet 42.917
## All.X#YeoJohnson#rcv#glmnet 45.222
## All.X#expoTrans#rcv#glmnet 51.149## label step_major step_minor label_minor bgn end
## 4 fit.models_1_preProc 1 3 preProc 13.266 534.995
## 5 fit.models_1_end 1 4 teardown 534.995 NA
## elapsed
## 4 521.729
## 5 NA## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1 1 0 0 9.967 535.005 525.038
## 2 fit.models 1 1 1 535.006 NA NA```{r fit.models_2, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}
## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 588.289 NA NA## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"## Warning: Removed 3 rows containing missing values (geom_errorbar).## quartz_off_screen
## 2## Warning: Removed 3 rows containing missing values (geom_errorbar).
## id max.Accuracy.OOB max.AUCROCR.OOB
## 20 All.X#expoTrans.spatialSign#rcv#glmnet 0.5936255 0.5756912
## 18 All.X#spatialSign#rcv#glmnet 0.5936255 0.5704991
## 11 All.X#expoTrans#rcv#glmnet 0.5916335 0.5697040
## 6 All.X##rcv#glmnet 0.5896414 0.5814512
## 10 All.X#YeoJohnson#rcv#glmnet 0.5876494 0.5696554
## 19 All.X#conditionalX#rcv#glmnet 0.5856574 0.5688766
## 5 Low.cor.X##rcv#glmnet 0.5856574 0.5688766
## 13 All.X#scale#rcv#glmnet 0.5856574 0.5688766
## 7 All.X#zv#rcv#glmnet 0.5856574 0.5688766
## 15 All.X#range#rcv#glmnet 0.5856574 0.5688766
## 12 All.X#center#rcv#glmnet 0.5856574 0.5688766
## 14 All.X#center.scale#rcv#glmnet 0.5856574 0.5688766
## 9 All.X#BoxCox#rcv#glmnet 0.5856574 0.5688766
## 8 All.X#nzv#rcv#glmnet 0.5796813 0.5784657
## 16 All.X#zv.pca#rcv#glmnet 0.5796813 0.5588331
## 3 Max.cor.Y.rcv.1X1###glmnet 0.5756972 0.5719513
## 4 Max.cor.Y##rcv#rpart 0.5737052 0.5452362
## 17 All.X#ica#rcv#glmnet 0.5737052 0.5297573
## 2 Random###myrandom_classfr 0.5737052 0.5181075
## 1 MFO###myMFO_classfr 0.5737052 0.5000000
## max.AUCpROC.OOB min.elapsedtime.everything max.Accuracy.fit
## 20 0.5600500 42.917 0.6128471
## 18 0.5554420 20.544 0.6118257
## 11 0.5554420 51.149 0.6109764
## 6 0.5541115 13.519 0.6126764
## 10 0.5554420 45.222 0.6109767
## 19 0.5554420 13.941 0.6128487
## 5 0.5554420 14.590 0.6128487
## 13 0.5554420 14.819 0.6128487
## 7 0.5554420 14.938 0.6128487
## 15 0.5554420 15.454 0.6128487
## 12 0.5554420 15.479 0.6128487
## 14 0.5554420 16.964 0.6128487
## 9 0.5554420 17.604 0.6128487
## 8 0.5436299 17.988 0.6087590
## 16 0.5017848 42.121 0.5959830
## 3 0.5468912 0.766 0.6152274
## 4 0.5468912 1.468 0.6136958
## 17 0.4820223 22.835 0.5918896
## 2 0.4990752 0.272 0.5789474
## 1 0.5000000 0.501 0.5789474
## opt.prob.threshold.fit opt.prob.threshold.OOB
## 20 0.55 0.45
## 18 0.55 0.45
## 11 0.55 0.45
## 6 0.55 0.50
## 10 0.50 0.45
## 19 0.55 0.50
## 5 0.55 0.50
## 13 0.55 0.50
## 7 0.55 0.50
## 15 0.55 0.50
## 12 0.55 0.50
## 14 0.55 0.50
## 9 0.55 0.50
## 8 0.55 0.50
## 16 0.55 0.55
## 3 0.50 0.55
## 4 0.50 0.40
## 17 0.50 0.40
## 2 0.40 0.40
## 1 0.40 0.40## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB + min.elapsedtime.everything -
## max.Accuracy.fit - opt.prob.threshold.OOB
## <environment: 0x7f8104f24478>## [1] "Best model id: All.X#expoTrans.spatialSign#rcv#glmnet"## glmnet
##
## 1957 samples
## 108 predictor
## 2 classes: 'D', 'R'
##
## Pre-processing: exponential transformation (260), spatial
## sign transformation (260), centered (260), scaled (260)
## Resampling: Cross-Validated (3 fold, repeated 3 times)
## Summary of sample sizes: 1305, 1305, 1304, 1304, 1305, 1305, ...
## Resampling results across tuning parameters:
##
## alpha lambda Accuracy Kappa
## 0.100 0.00720660 0.5631097 0.087759034
## 0.100 0.02000000 0.5792927 0.113087076
## 0.100 0.03345007 0.5901938 0.130012436
## 0.100 0.04000000 0.5934301 0.134657686
## 0.100 0.05969355 0.6014363 0.146386040
## 0.325 0.00720660 0.5758878 0.108347798
## 0.325 0.02000000 0.5961586 0.138436776
## 0.325 0.03345007 0.6109746 0.160956457
## 0.325 0.04000000 0.6128471 0.160507954
## 0.325 0.05969355 0.6084176 0.137167646
## 0.550 0.00720660 0.5850845 0.122792572
## 0.550 0.02000000 0.6097817 0.159653338
## 0.550 0.03345007 0.6090990 0.144015377
## 0.550 0.04000000 0.6038174 0.125422454
## 0.550 0.05969355 0.5920632 0.069661928
## 0.775 0.00720660 0.5936032 0.137960895
## 0.775 0.02000000 0.6109725 0.155406200
## 0.775 0.03345007 0.6017724 0.114649568
## 0.775 0.04000000 0.5964919 0.088067777
## 0.775 0.05969355 0.5784372 0.003098888
## 1.000 0.00720660 0.5949665 0.137113011
## 1.000 0.02000000 0.6077357 0.140151511
## 1.000 0.03345007 0.5922325 0.074264296
## 1.000 0.04000000 0.5821835 0.029798428
## 1.000 0.05969355 0.5789474 0.000000000
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were alpha = 0.325 and lambda = 0.04.## [1] "All.X#expoTrans.spatialSign#rcv#glmnet fit prediction diagnostics:"## [1] "All.X#expoTrans.spatialSign#rcv#glmnet OOB prediction diagnostics:"## All.X.expoTrans.spatialSign.rcv.glmnet.imp
## Q115611.fctrYes 100.0000000
## Q113181.fctrNo 95.3534018
## Hhold.fctrPKn 72.3736413
## Q116881.fctrRight 63.5102201
## Q99480.fctrNo 54.7705038
## Q98869.fctrNo 53.8392977
## Q106997.fctrGr 53.8293468
## Q101163.fctrDad 52.9462718
## Q98197.fctrNo 51.2895374
## Hhold.fctrPKy 48.8973726
## Q113181.fctrYes 37.0837563
## Q119650.fctrGiving 35.7211724
## Q124742.fctrNo 33.9726792
## Q115611.fctrNo 33.9313892
## Hhold.fctrMKy 32.0285479
## Q106272.fctrNo 28.7696462
## Q105655.fctrYes 28.1499086
## Q114517.fctrNo 26.3106333
## Q123621.fctrYes 24.3374872
## Hhold.fctrPKy:.clusterid.fctr4 24.0708883
## Income.fctr.C 23.6556411
## Q100562.fctrNo 21.9532492
## Q109367.fctrNo 21.4635778
## Q120472.fctrScience 19.0457801
## Income.fctr.Q 18.7794022
## Q115899.fctrCs 17.7016232
## Q118892.fctrNo 16.9276058
## Q116881.fctrHappy 16.8262843
## Q110740.fctrPC 16.2478906
## YOB.Age.fctr(35,40]:YOB.Age.dff 14.9297986
## Hhold.fctrSKy 14.5868679
## Income.fctr^6 14.3529560
## Q108855.fctrUmm... 14.1070607
## Hhold.fctrSKy:.clusterid.fctr3 13.9491938
## Q104996.fctrYes 13.3267802
## Hhold.fctrN:.clusterid.fctr4 13.2538905
## Hhold.fctrMKy:.clusterid.fctr4 13.1648041
## Q99716.fctrYes 12.6078177
## Q116953.fctrNo 12.4240036
## Hhold.fctrSKn:.clusterid.fctr2 11.3977291
## Q109367.fctrYes 8.8146390
## Q114386.fctrMysterious 7.6865479
## Q116601.fctrNo 5.7800906
## Q120379.fctrYes 5.4427713
## Q102089.fctrOwn 4.0312831
## Q122769.fctrYes 3.6547887
## Q117193.fctrStandard hours 3.5026709
## Q96024.fctrNo 1.1407544
## Q108855.fctrYes! 1.0727486
## Q106272.fctrYes 0.9188083
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.6262704
## Q122771.fctrPt 0.4572496
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.1756714
## Q99480.fctrYes 0.1700106
## Q100689.fctrYes 0.1638523
## .rnorm 0.0000000
## Edn.fctr.L 0.0000000
## Edn.fctr.Q 0.0000000
## Edn.fctr.C 0.0000000
## Edn.fctr^4 0.0000000
## Edn.fctr^5 0.0000000
## Edn.fctr^6 0.0000000
## Edn.fctr^7 0.0000000
## Gender.fctrF 0.0000000
## Gender.fctrM 0.0000000
## Hhold.fctrMKn 0.0000000
## Hhold.fctrSKn 0.0000000
## Income.fctr.L 0.0000000
## Income.fctr^4 0.0000000
## Income.fctr^5 0.0000000
## Q100010.fctrNo 0.0000000
## Q100010.fctrYes 0.0000000
## Q100562.fctrYes 0.0000000
## Q100680.fctrNo 0.0000000
## Q100680.fctrYes 0.0000000
## Q100689.fctrNo 0.0000000
## Q101162.fctrOptimist 0.0000000
## Q101162.fctrPessimist 0.0000000
## Q101163.fctrMom 0.0000000
## Q101596.fctrNo 0.0000000
## Q101596.fctrYes 0.0000000
## Q102089.fctrRent 0.0000000
## Q102289.fctrNo 0.0000000
## Q102289.fctrYes 0.0000000
## Q102674.fctrNo 0.0000000
## Q102674.fctrYes 0.0000000
## Q102687.fctrNo 0.0000000
## Q102687.fctrYes 0.0000000
## Q102906.fctrNo 0.0000000
## Q102906.fctrYes 0.0000000
## Q103293.fctrNo 0.0000000
## Q103293.fctrYes 0.0000000
## Q104996.fctrNo 0.0000000
## Q105655.fctrNo 0.0000000
## Q105840.fctrNo 0.0000000
## Q105840.fctrYes 0.0000000
## Q106042.fctrNo 0.0000000
## Q106042.fctrYes 0.0000000
## Q106388.fctrNo 0.0000000
## Q106388.fctrYes 0.0000000
## Q106389.fctrNo 0.0000000
## Q106389.fctrYes 0.0000000
## Q106993.fctrNo 0.0000000
## Q106993.fctrYes 0.0000000
## Q106997.fctrYy 0.0000000
## Q107491.fctrNo 0.0000000
## Q107491.fctrYes 0.0000000
## Q107869.fctrNo 0.0000000
## Q107869.fctrYes 0.0000000
## Q108342.fctrIn-person 0.0000000
## Q108342.fctrOnline 0.0000000
## Q108343.fctrNo 0.0000000
## Q108343.fctrYes 0.0000000
## Q108617.fctrNo 0.0000000
## Q108617.fctrYes 0.0000000
## Q108754.fctrNo 0.0000000
## Q108754.fctrYes 0.0000000
## Q108856.fctrSocialize 0.0000000
## Q108856.fctrSpace 0.0000000
## Q108950.fctrCautious 0.0000000
## Q108950.fctrRisk-friendly 0.0000000
## Q110740.fctrMac 0.0000000
## Q111220.fctrNo 0.0000000
## Q111220.fctrYes 0.0000000
## Q111580.fctrDemanding 0.0000000
## Q111580.fctrSupportive 0.0000000
## Q111848.fctrNo 0.0000000
## Q111848.fctrYes 0.0000000
## Q112270.fctrNo 0.0000000
## Q112270.fctrYes 0.0000000
## Q112478.fctrNo 0.0000000
## Q112478.fctrYes 0.0000000
## Q112512.fctrNo 0.0000000
## Q112512.fctrYes 0.0000000
## Q113583.fctrTalk 0.0000000
## Q113583.fctrTunes 0.0000000
## Q113584.fctrPeople 0.0000000
## Q113584.fctrTechnology 0.0000000
## Q113992.fctrNo 0.0000000
## Q113992.fctrYes 0.0000000
## Q114152.fctrNo 0.0000000
## Q114152.fctrYes 0.0000000
## Q114386.fctrTMI 0.0000000
## Q114517.fctrYes 0.0000000
## Q114748.fctrNo 0.0000000
## Q114748.fctrYes 0.0000000
## Q114961.fctrNo 0.0000000
## Q114961.fctrYes 0.0000000
## Q115195.fctrNo 0.0000000
## Q115195.fctrYes 0.0000000
## Q115390.fctrNo 0.0000000
## Q115390.fctrYes 0.0000000
## Q115602.fctrNo 0.0000000
## Q115602.fctrYes 0.0000000
## Q115610.fctrNo 0.0000000
## Q115610.fctrYes 0.0000000
## Q115777.fctrEnd 0.0000000
## Q115777.fctrStart 0.0000000
## Q115899.fctrMe 0.0000000
## Q116197.fctrA.M. 0.0000000
## Q116197.fctrP.M. 0.0000000
## Q116441.fctrNo 0.0000000
## Q116441.fctrYes 0.0000000
## Q116448.fctrNo 0.0000000
## Q116448.fctrYes 0.0000000
## Q116601.fctrYes 0.0000000
## Q116797.fctrNo 0.0000000
## Q116797.fctrYes 0.0000000
## Q116953.fctrYes 0.0000000
## Q117186.fctrCool headed 0.0000000
## Q117186.fctrHot headed 0.0000000
## Q117193.fctrOdd hours 0.0000000
## Q118117.fctrNo 0.0000000
## Q118117.fctrYes 0.0000000
## Q118232.fctrId 0.0000000
## Q118232.fctrPr 0.0000000
## Q118233.fctrNo 0.0000000
## Q118233.fctrYes 0.0000000
## Q118237.fctrNo 0.0000000
## Q118237.fctrYes 0.0000000
## Q118892.fctrYes 0.0000000
## Q119334.fctrNo 0.0000000
## Q119334.fctrYes 0.0000000
## Q119650.fctrReceiving 0.0000000
## Q119851.fctrNo 0.0000000
## Q119851.fctrYes 0.0000000
## Q120012.fctrNo 0.0000000
## Q120012.fctrYes 0.0000000
## Q120014.fctrNo 0.0000000
## Q120014.fctrYes 0.0000000
## Q120194.fctrStudy first 0.0000000
## Q120194.fctrTry first 0.0000000
## Q120379.fctrNo 0.0000000
## Q120472.fctrArt 0.0000000
## Q120650.fctrNo 0.0000000
## Q120650.fctrYes 0.0000000
## Q120978.fctrNo 0.0000000
## Q120978.fctrYes 0.0000000
## Q121011.fctrNo 0.0000000
## Q121011.fctrYes 0.0000000
## Q121699.fctrNo 0.0000000
## Q121699.fctrYes 0.0000000
## Q121700.fctrNo 0.0000000
## Q121700.fctrYes 0.0000000
## Q122120.fctrNo 0.0000000
## Q122120.fctrYes 0.0000000
## Q122769.fctrNo 0.0000000
## Q122770.fctrNo 0.0000000
## Q122770.fctrYes 0.0000000
## Q122771.fctrPc 0.0000000
## Q123464.fctrNo 0.0000000
## Q123464.fctrYes 0.0000000
## Q123621.fctrNo 0.0000000
## Q124122.fctrNo 0.0000000
## Q124122.fctrYes 0.0000000
## Q124742.fctrYes 0.0000000
## Q96024.fctrYes 0.0000000
## Q98059.fctrOnly-child 0.0000000
## Q98059.fctrYes 0.0000000
## Q98078.fctrNo 0.0000000
## Q98078.fctrYes 0.0000000
## Q98197.fctrYes 0.0000000
## Q98578.fctrNo 0.0000000
## Q98578.fctrYes 0.0000000
## Q98869.fctrYes 0.0000000
## Q99581.fctrNo 0.0000000
## Q99581.fctrYes 0.0000000
## Q99716.fctrNo 0.0000000
## Q99982.fctrCheck! 0.0000000
## Q99982.fctrNope 0.0000000
## YOB.Age.fctr.L 0.0000000
## YOB.Age.fctr.Q 0.0000000
## YOB.Age.fctr.C 0.0000000
## YOB.Age.fctr^4 0.0000000
## YOB.Age.fctr^5 0.0000000
## YOB.Age.fctr^6 0.0000000
## YOB.Age.fctr^7 0.0000000
## YOB.Age.fctr^8 0.0000000
## Hhold.fctrN:.clusterid.fctr2 0.0000000
## Hhold.fctrMKn:.clusterid.fctr2 0.0000000
## Hhold.fctrMKy:.clusterid.fctr2 0.0000000
## Hhold.fctrPKn:.clusterid.fctr2 0.0000000
## Hhold.fctrPKy:.clusterid.fctr2 0.0000000
## Hhold.fctrSKy:.clusterid.fctr2 0.0000000
## Hhold.fctrN:.clusterid.fctr3 0.0000000
## Hhold.fctrMKn:.clusterid.fctr3 0.0000000
## Hhold.fctrMKy:.clusterid.fctr3 0.0000000
## Hhold.fctrPKn:.clusterid.fctr3 0.0000000
## Hhold.fctrPKy:.clusterid.fctr3 0.0000000
## Hhold.fctrSKn:.clusterid.fctr3 0.0000000
## Hhold.fctrMKn:.clusterid.fctr4 0.0000000
## Hhold.fctrPKn:.clusterid.fctr4 0.0000000
## Hhold.fctrSKn:.clusterid.fctr4 0.0000000
## Hhold.fctrSKy:.clusterid.fctr4 0.0000000
## YOB.Age.fctrNA:YOB.Age.dff 0.0000000
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.0000000
## imp
## Q115611.fctrYes 100.0000000
## Q113181.fctrNo 95.3534018
## Hhold.fctrPKn 72.3736413
## Q116881.fctrRight 63.5102201
## Q99480.fctrNo 54.7705038
## Q98869.fctrNo 53.8392977
## Q106997.fctrGr 53.8293468
## Q101163.fctrDad 52.9462718
## Q98197.fctrNo 51.2895374
## Hhold.fctrPKy 48.8973726
## Q113181.fctrYes 37.0837563
## Q119650.fctrGiving 35.7211724
## Q124742.fctrNo 33.9726792
## Q115611.fctrNo 33.9313892
## Hhold.fctrMKy 32.0285479
## Q106272.fctrNo 28.7696462
## Q105655.fctrYes 28.1499086
## Q114517.fctrNo 26.3106333
## Q123621.fctrYes 24.3374872
## Hhold.fctrPKy:.clusterid.fctr4 24.0708883
## Income.fctr.C 23.6556411
## Q100562.fctrNo 21.9532492
## Q109367.fctrNo 21.4635778
## Q120472.fctrScience 19.0457801
## Income.fctr.Q 18.7794022
## Q115899.fctrCs 17.7016232
## Q118892.fctrNo 16.9276058
## Q116881.fctrHappy 16.8262843
## Q110740.fctrPC 16.2478906
## YOB.Age.fctr(35,40]:YOB.Age.dff 14.9297986
## Hhold.fctrSKy 14.5868679
## Income.fctr^6 14.3529560
## Q108855.fctrUmm... 14.1070607
## Hhold.fctrSKy:.clusterid.fctr3 13.9491938
## Q104996.fctrYes 13.3267802
## Hhold.fctrN:.clusterid.fctr4 13.2538905
## Hhold.fctrMKy:.clusterid.fctr4 13.1648041
## Q99716.fctrYes 12.6078177
## Q116953.fctrNo 12.4240036
## Hhold.fctrSKn:.clusterid.fctr2 11.3977291
## Q109367.fctrYes 8.8146390
## Q114386.fctrMysterious 7.6865479
## Q116601.fctrNo 5.7800906
## Q120379.fctrYes 5.4427713
## Q102089.fctrOwn 4.0312831
## Q122769.fctrYes 3.6547887
## Q117193.fctrStandard hours 3.5026709
## Q96024.fctrNo 1.1407544
## Q108855.fctrYes! 1.0727486
## Q106272.fctrYes 0.9188083
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.6262704
## Q122771.fctrPt 0.4572496
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.1756714
## Q99480.fctrYes 0.1700106
## Q100689.fctrYes 0.1638523
## .rnorm 0.0000000
## Edn.fctr.L 0.0000000
## Edn.fctr.Q 0.0000000
## Edn.fctr.C 0.0000000
## Edn.fctr^4 0.0000000
## Edn.fctr^5 0.0000000
## Edn.fctr^6 0.0000000
## Edn.fctr^7 0.0000000
## Gender.fctrF 0.0000000
## Gender.fctrM 0.0000000
## Hhold.fctrMKn 0.0000000
## Hhold.fctrSKn 0.0000000
## Income.fctr.L 0.0000000
## Income.fctr^4 0.0000000
## Income.fctr^5 0.0000000
## Q100010.fctrNo 0.0000000
## Q100010.fctrYes 0.0000000
## Q100562.fctrYes 0.0000000
## Q100680.fctrNo 0.0000000
## Q100680.fctrYes 0.0000000
## Q100689.fctrNo 0.0000000
## Q101162.fctrOptimist 0.0000000
## Q101162.fctrPessimist 0.0000000
## Q101163.fctrMom 0.0000000
## Q101596.fctrNo 0.0000000
## Q101596.fctrYes 0.0000000
## Q102089.fctrRent 0.0000000
## Q102289.fctrNo 0.0000000
## Q102289.fctrYes 0.0000000
## Q102674.fctrNo 0.0000000
## Q102674.fctrYes 0.0000000
## Q102687.fctrNo 0.0000000
## Q102687.fctrYes 0.0000000
## Q102906.fctrNo 0.0000000
## Q102906.fctrYes 0.0000000
## Q103293.fctrNo 0.0000000
## Q103293.fctrYes 0.0000000
## Q104996.fctrNo 0.0000000
## Q105655.fctrNo 0.0000000
## Q105840.fctrNo 0.0000000
## Q105840.fctrYes 0.0000000
## Q106042.fctrNo 0.0000000
## Q106042.fctrYes 0.0000000
## Q106388.fctrNo 0.0000000
## Q106388.fctrYes 0.0000000
## Q106389.fctrNo 0.0000000
## Q106389.fctrYes 0.0000000
## Q106993.fctrNo 0.0000000
## Q106993.fctrYes 0.0000000
## Q106997.fctrYy 0.0000000
## Q107491.fctrNo 0.0000000
## Q107491.fctrYes 0.0000000
## Q107869.fctrNo 0.0000000
## Q107869.fctrYes 0.0000000
## Q108342.fctrIn-person 0.0000000
## Q108342.fctrOnline 0.0000000
## Q108343.fctrNo 0.0000000
## Q108343.fctrYes 0.0000000
## Q108617.fctrNo 0.0000000
## Q108617.fctrYes 0.0000000
## Q108754.fctrNo 0.0000000
## Q108754.fctrYes 0.0000000
## Q108856.fctrSocialize 0.0000000
## Q108856.fctrSpace 0.0000000
## Q108950.fctrCautious 0.0000000
## Q108950.fctrRisk-friendly 0.0000000
## Q110740.fctrMac 0.0000000
## Q111220.fctrNo 0.0000000
## Q111220.fctrYes 0.0000000
## Q111580.fctrDemanding 0.0000000
## Q111580.fctrSupportive 0.0000000
## Q111848.fctrNo 0.0000000
## Q111848.fctrYes 0.0000000
## Q112270.fctrNo 0.0000000
## Q112270.fctrYes 0.0000000
## Q112478.fctrNo 0.0000000
## Q112478.fctrYes 0.0000000
## Q112512.fctrNo 0.0000000
## Q112512.fctrYes 0.0000000
## Q113583.fctrTalk 0.0000000
## Q113583.fctrTunes 0.0000000
## Q113584.fctrPeople 0.0000000
## Q113584.fctrTechnology 0.0000000
## Q113992.fctrNo 0.0000000
## Q113992.fctrYes 0.0000000
## Q114152.fctrNo 0.0000000
## Q114152.fctrYes 0.0000000
## Q114386.fctrTMI 0.0000000
## Q114517.fctrYes 0.0000000
## Q114748.fctrNo 0.0000000
## Q114748.fctrYes 0.0000000
## Q114961.fctrNo 0.0000000
## Q114961.fctrYes 0.0000000
## Q115195.fctrNo 0.0000000
## Q115195.fctrYes 0.0000000
## Q115390.fctrNo 0.0000000
## Q115390.fctrYes 0.0000000
## Q115602.fctrNo 0.0000000
## Q115602.fctrYes 0.0000000
## Q115610.fctrNo 0.0000000
## Q115610.fctrYes 0.0000000
## Q115777.fctrEnd 0.0000000
## Q115777.fctrStart 0.0000000
## Q115899.fctrMe 0.0000000
## Q116197.fctrA.M. 0.0000000
## Q116197.fctrP.M. 0.0000000
## Q116441.fctrNo 0.0000000
## Q116441.fctrYes 0.0000000
## Q116448.fctrNo 0.0000000
## Q116448.fctrYes 0.0000000
## Q116601.fctrYes 0.0000000
## Q116797.fctrNo 0.0000000
## Q116797.fctrYes 0.0000000
## Q116953.fctrYes 0.0000000
## Q117186.fctrCool headed 0.0000000
## Q117186.fctrHot headed 0.0000000
## Q117193.fctrOdd hours 0.0000000
## Q118117.fctrNo 0.0000000
## Q118117.fctrYes 0.0000000
## Q118232.fctrId 0.0000000
## Q118232.fctrPr 0.0000000
## Q118233.fctrNo 0.0000000
## Q118233.fctrYes 0.0000000
## Q118237.fctrNo 0.0000000
## Q118237.fctrYes 0.0000000
## Q118892.fctrYes 0.0000000
## Q119334.fctrNo 0.0000000
## Q119334.fctrYes 0.0000000
## Q119650.fctrReceiving 0.0000000
## Q119851.fctrNo 0.0000000
## Q119851.fctrYes 0.0000000
## Q120012.fctrNo 0.0000000
## Q120012.fctrYes 0.0000000
## Q120014.fctrNo 0.0000000
## Q120014.fctrYes 0.0000000
## Q120194.fctrStudy first 0.0000000
## Q120194.fctrTry first 0.0000000
## Q120379.fctrNo 0.0000000
## Q120472.fctrArt 0.0000000
## Q120650.fctrNo 0.0000000
## Q120650.fctrYes 0.0000000
## Q120978.fctrNo 0.0000000
## Q120978.fctrYes 0.0000000
## Q121011.fctrNo 0.0000000
## Q121011.fctrYes 0.0000000
## Q121699.fctrNo 0.0000000
## Q121699.fctrYes 0.0000000
## Q121700.fctrNo 0.0000000
## Q121700.fctrYes 0.0000000
## Q122120.fctrNo 0.0000000
## Q122120.fctrYes 0.0000000
## Q122769.fctrNo 0.0000000
## Q122770.fctrNo 0.0000000
## Q122770.fctrYes 0.0000000
## Q122771.fctrPc 0.0000000
## Q123464.fctrNo 0.0000000
## Q123464.fctrYes 0.0000000
## Q123621.fctrNo 0.0000000
## Q124122.fctrNo 0.0000000
## Q124122.fctrYes 0.0000000
## Q124742.fctrYes 0.0000000
## Q96024.fctrYes 0.0000000
## Q98059.fctrOnly-child 0.0000000
## Q98059.fctrYes 0.0000000
## Q98078.fctrNo 0.0000000
## Q98078.fctrYes 0.0000000
## Q98197.fctrYes 0.0000000
## Q98578.fctrNo 0.0000000
## Q98578.fctrYes 0.0000000
## Q98869.fctrYes 0.0000000
## Q99581.fctrNo 0.0000000
## Q99581.fctrYes 0.0000000
## Q99716.fctrNo 0.0000000
## Q99982.fctrCheck! 0.0000000
## Q99982.fctrNope 0.0000000
## YOB.Age.fctr.L 0.0000000
## YOB.Age.fctr.Q 0.0000000
## YOB.Age.fctr.C 0.0000000
## YOB.Age.fctr^4 0.0000000
## YOB.Age.fctr^5 0.0000000
## YOB.Age.fctr^6 0.0000000
## YOB.Age.fctr^7 0.0000000
## YOB.Age.fctr^8 0.0000000
## Hhold.fctrN:.clusterid.fctr2 0.0000000
## Hhold.fctrMKn:.clusterid.fctr2 0.0000000
## Hhold.fctrMKy:.clusterid.fctr2 0.0000000
## Hhold.fctrPKn:.clusterid.fctr2 0.0000000
## Hhold.fctrPKy:.clusterid.fctr2 0.0000000
## Hhold.fctrSKy:.clusterid.fctr2 0.0000000
## Hhold.fctrN:.clusterid.fctr3 0.0000000
## Hhold.fctrMKn:.clusterid.fctr3 0.0000000
## Hhold.fctrMKy:.clusterid.fctr3 0.0000000
## Hhold.fctrPKn:.clusterid.fctr3 0.0000000
## Hhold.fctrPKy:.clusterid.fctr3 0.0000000
## Hhold.fctrSKn:.clusterid.fctr3 0.0000000
## Hhold.fctrMKn:.clusterid.fctr4 0.0000000
## Hhold.fctrPKn:.clusterid.fctr4 0.0000000
## Hhold.fctrSKn:.clusterid.fctr4 0.0000000
## Hhold.fctrSKy:.clusterid.fctr4 0.0000000
## YOB.Age.fctrNA:YOB.Age.dff 0.0000000
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.0000000## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 108
## Loading required package: lazyeval
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr
## 1 470 R
## 2 1836 R
## 3 3312 R
## 4 5379 R
## 5 183 R
## 6 4146 R
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.prob
## 1 0.3004303
## 2 0.3365100
## 3 0.3418227
## 4 0.3506066
## 5 0.3773732
## 6 0.3821215
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet
## 1 D
## 2 D
## 3 D
## 4 D
## 5 D
## 6 D
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.err
## 1 TRUE
## 2 TRUE
## 3 TRUE
## 4 TRUE
## 5 TRUE
## 6 TRUE
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.err.abs
## 1 0.6995697
## 2 0.6634900
## 3 0.6581773
## 4 0.6493934
## 5 0.6226268
## 6 0.6178785
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.accurate
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.error
## 1 -0.14956970
## 2 -0.11349004
## 3 -0.10817725
## 4 -0.09939345
## 5 -0.07262680
## 6 -0.06787850
## USER_ID Party.fctr
## 23 4017 R
## 39 526 D
## 46 1979 D
## 49 1038 D
## 127 3644 D
## 160 6107 D
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.prob
## 23 0.4324538
## 39 0.4650899
## 46 0.4760947
## 49 0.4806819
## 127 0.5942744
## 160 0.6392700
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet
## 23 D
## 39 R
## 46 R
## 49 R
## 127 R
## 160 R
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.err
## 23 TRUE
## 39 TRUE
## 46 TRUE
## 49 TRUE
## 127 TRUE
## 160 TRUE
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.err.abs
## 23 0.5675462
## 39 0.4650899
## 46 0.4760947
## 49 0.4806819
## 127 0.5942744
## 160 0.6392700
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.is.acc
## 23 FALSE
## 39 FALSE
## 46 FALSE
## 49 FALSE
## 127 FALSE
## 160 FALSE
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.accurate
## 23 FALSE
## 39 FALSE
## 46 FALSE
## 49 FALSE
## 127 FALSE
## 160 FALSE
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.error
## 23 -0.01754624
## 39 0.01508989
## 46 0.02609467
## 49 0.03068186
## 127 0.14427439
## 160 0.18926996
## USER_ID Party.fctr
## 199 4956 D
## 200 1207 D
## 201 2798 D
## 202 3474 D
## 203 3221 D
## 204 1311 D
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.prob
## 199 0.7461831
## 200 0.7465315
## 201 0.7549233
## 202 0.7552433
## 203 0.7590532
## 204 0.7794015
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet
## 199 R
## 200 R
## 201 R
## 202 R
## 203 R
## 204 R
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.err
## 199 TRUE
## 200 TRUE
## 201 TRUE
## 202 TRUE
## 203 TRUE
## 204 TRUE
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.err.abs
## 199 0.7461831
## 200 0.7465315
## 201 0.7549233
## 202 0.7552433
## 203 0.7590532
## 204 0.7794015
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.is.acc
## 199 FALSE
## 200 FALSE
## 201 FALSE
## 202 FALSE
## 203 FALSE
## 204 FALSE
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.accurate
## 199 FALSE
## 200 FALSE
## 201 FALSE
## 202 FALSE
## 203 FALSE
## 204 FALSE
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.error
## 199 0.2961831
## 200 0.2965315
## 201 0.3049233
## 202 0.3052433
## 203 0.3090532
## 204 0.3294015
## Hhold.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## PKn PKn 13 50 15 0.025549310 0.02589641
## PKy PKy 6 14 6 0.007153807 0.01195219
## SKy SKy 23 61 27 0.031170158 0.04581673
## SKn SKn 201 810 252 0.413898825 0.40039841
## MKy MKy 156 680 195 0.347470618 0.31075697
## MKn MKn 75 226 94 0.115482882 0.14940239
## N N 28 116 33 0.059274400 0.05577689
## .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## PKn 0.024115756 22.893872 0.4578774 50 7.134215
## PKy 0.009646302 6.299374 0.4499553 14 2.971012
## SKy 0.043408360 28.827288 0.4725785 61 11.348368
## SKn 0.405144695 373.399097 0.4609865 810 97.262331
## MKy 0.313504823 300.522687 0.4419451 680 73.031659
## MKn 0.151125402 103.615614 0.4584762 226 34.933708
## N 0.053054662 53.063882 0.4574473 116 12.946810
## err.abs.OOB.mean
## PKn 0.5487857
## PKy 0.4951687
## SKy 0.4934073
## SKn 0.4838922
## MKy 0.4681517
## MKn 0.4657828
## N 0.4623861
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 502.000000 1957.000000 622.000000 1.000000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.000000 1.000000 888.621814 3.199266
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 1957.000000 239.628103 3.417574## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 597.149 NA NA## label step_major step_minor label_minor bgn end elapsed
## 2 fit.models 1 1 1 535.006 597.16 62.154
## 3 fit.models 1 2 2 597.161 NA NA# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 3 fit.models 1 2 2 597.161 600.612
## 4 fit.data.training 2 0 0 600.612 NA
## elapsed
## 3 3.451
## 4 NAStep 2.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (!is.null(glbMdlFinId) && (glbMdlFinId %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_models_lst[[glbMdlFinId]]
} else
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var]))) {
warning("Final model same as glbMdlSelId")
glbMdlFinId <- paste0("Final.", glbMdlSelId)
glb_fin_mdl <- glb_sel_mdl
glb_models_lst[[glbMdlFinId]] <- glb_fin_mdl
mdlDf <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
mdlDf$id <- glbMdlFinId
glb_models_df <- rbind(glb_models_df, mdlDf)
} else {
if (myparseMdlId(glbMdlSelId)$family == "RFE.X") {
indepVar <- mygetIndepVar(glb_feats_df)
trnRFEResults <-
myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
if (!isTRUE(all.equal(sort(predictors(trnRFEResults)),
sort(predictors(glbRFEResults))))) {
print("Diffs predictors(trnRFEResults) vs. predictors(glbRFEResults):")
print(setdiff(predictors(trnRFEResults), predictors(glbRFEResults)))
print("Diffs predictors(glbRFEResults) vs. predictors(trnRFEResults):")
print(setdiff(predictors(glbRFEResults), predictors(trnRFEResults)))
}
}
if (grepl("Ensemble", glbMdlSelId)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
mdlIndepVar <- row.names(mdlimp_df)
if (glb_is_classification)
mdlIdVcr <- glbMdlEnsemble[sapply(glbMdlEnsemble, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$prob %in% mdlIndepVar)] else
mdlIdVcr <- glbMdlEnsemble[sapply(glbMdlEnsemble, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$value %in% mdlIndepVar)]
# Fit selected models on glbObsTrn
for (mdl_id in mdlIdVcr) {
mdl_id_components <- myparseMdlId(mdl_id)
mdlIdPfx <- mdl_id_components$family
# if (grepl("RFE\\.X\\.", mdlIdPfx))
# mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
# predictors(trnRFEResults))) else
# mdlIndepVars <- trim(unlist(
# strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
thsIndepVar <- unlist(
strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]"))
thsSpc <- myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = paste0("Final.", mdlIdPfx),
type = glb_model_type, tune.df = glbMdlTuneParams,
trainControl.method = mdl_id_components$resample,
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = mdl_id_components$alg,
train.preProcess = mdl_id_components$preProcess))
ret_lst <- myfit_mdl(mdl_specs_lst = thsSpc,
indepVar = thsIndepVar,
rsp_var = glb_rsp_var,
fit_df = glbObsTrn, OOB_df = NULL)
glbObsTrn <- glb_get_predictions(df = glbObsTrn,
mdl_id = thsSpc$id,
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
glbObsNew <- glb_get_predictions(df = glbObsNew,
mdl_id = thsSpc$id,
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
}
}
# "Final" model
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the mdl_id
model_method <- tail(unlist(strsplit(glbMdlSelId, "[.]")), 1)
if (grepl("Ensemble", glbMdlSelId)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
mdlIndepVar <- row.names(mdlimp_df)
if (glb_is_classification)
mdlIdVcr <- glbMdlEnsemble[sapply(glbMdlEnsemble, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$prob %in% mdlIndepVar)] else
mdlIdVcr <- glbMdlEnsemble[sapply(glbMdlEnsemble, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$value %in% mdlIndepVar)]
mdlIdVcr <- paste("Final", mdlIdVcr, sep = ".")
mdlIndepVar <- gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"), mdlIndepVar, fixed = TRUE)
# if (glb_is_classification && glb_is_binomial)
# indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
# row.names(mdlimp_df)) else
# indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
# row.names(mdlimp_df))
} else
if (grepl("RFE.X", glbMdlSelId, fixed = TRUE)) {
# indepVar <- myextract_actual_feats(predictors(trnRFEResults))
mdlIndepVar <- myextract_actual_feats(predictors(glbRFEResults))
} else mdlIndepVar <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
glbMdlSelId
, "feats"], "[,]")))
# if (!is.null(glbMdlPreprocMethods) &&
# ((match_pos <- regexpr(gsub(".", "\\.",
# paste(glbMdlPreprocMethods, collapse = "|"),
# fixed = TRUE), glbMdlSelId)) != -1))
# ths_preProcess <- str_sub(glbMdlSelId, match_pos,
# match_pos + attr(match_pos, "match.length") - 1) else
# ths_preProcess <- NULL
# mdl_id_pfx <- ifelse(grepl("Ensemble", glbMdlSelId),
# "Final.Ensemble", "Final")
thsMdlId <- paste0("Final.", glbMdlSelId)
thsMdlIdComponents <- myparseMdlId(thsMdlId)
# mdl_id_pfx <- paste("Final", myparseMdlId(glbMdlSelId)$family, sep = ".")
mdl_id_pfx <- thsMdlIdComponents$family
trnobs_df <- glbObsTrn
if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
}
# Force fitting of Final.glm to identify outliers
# method_vctr <- unique(c(myparseMdlId(glbMdlSelId)$alg, glbMdlFamilies[["Final"]]))
thsSpc <- myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type, tune.df = glbMdlTuneParams,
trainControl.method = thsMdlIdComponents$resample,
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = thsMdlIdComponents$alg,
train.preProcess = thsMdlIdComponents$preProcess))
glbMdlFinId <- thsSpc$id
if (!(grepl("Ensemble", glbMdlSelId)))
ret_lst <- myfit_mdl(mdl_specs_lst = thsSpc,
indepVar = mdlIndepVar,
rsp_var = glb_rsp_var,
fit_df = glbObsTrn, OOB_df = NULL) else {
# Final model same as selected model except for the model features
tmp_models_df <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
tmp_models_df$id <- paste0("Final.", tmp_models_df$id)
row.names(tmp_models_df) <- tmp_models_df$id
tmp_models_df$feats <- gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"),
tmp_models_df$feats, fixed = TRUE)
glb_models_df <- rbind(glb_models_df, tmp_models_df)
tmp_fin_mdl <- glb_sel_mdl
# tmp_fin_mdl$coefnames <- gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"),
# tmp_fin_mdl$coefnames, fixed = TRUE)
# dimnames(tmp_fin_mdl$finalModel$beta)[[1]] <-
# gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"),
# dimnames(tmp_fin_mdl$finalModel$beta)[[1]], fixed = TRUE)
# tmp_fin_mdl$finalModel$xNames <-
# gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"),
# tmp_fin_mdl$finalModel$xNames, fixed = TRUE)
#
# thsAts <- attributes(tmp_fin_mdl$terms)
# # thsAts$variables <- class == "call" & objects / symbols are stored as a formula
# thsAts$term.labels <-
# gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"),
# thsAts$term.labels, fixed = TRUE)
# attributes(tmp_fin_mdl$terms) <- thsAts
#
glb_models_lst[[glbMdlFinId]] <- tmp_fin_mdl
}
glb_fin_mdl <- glb_models_lst[[glbMdlFinId]]
}## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Final.All.X#expoTrans.spatialSign#rcv#glmnet"
## [1] " indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.691000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.0301 on full training set## Warning in preProcess.default(thresh = 0.95, k = 5, method
## = c("expoTrans", : These variables have zero variances:
## Hhold.fctrPKn:.clusterid.fctr4, Hhold.fctrSKn:.clusterid.fctr4,
## Hhold.fctrSKy:.clusterid.fctr4, YOB.Age.fctrNA:YOB.Age.dff## [1] "myfit_mdl: train complete: 49.684000 secs"
## Length Class Mode
## a0 79 -none- numeric
## beta 20540 dgCMatrix S4
## df 79 -none- numeric
## dim 2 -none- numeric
## lambda 79 -none- numeric
## dev.ratio 79 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 260 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.31967158 0.17044108
## Hhold.fctrPKn Hhold.fctrPKy
## -0.70076433 -0.43785935
## Income.fctr.Q Income.fctr^6
## 0.13626004 -0.07125976
## Q101163.fctrDad Q106272.fctrNo
## 0.94827993 -0.28578376
## Q106997.fctrGr Q108855.fctrYes!
## 0.44710755 0.27724752
## Q110740.fctrPC Q113181.fctrNo
## 0.44766624 -1.48092661
## Q113181.fctrYes Q115611.fctrNo
## 0.76849888 -0.72420162
## Q115611.fctrYes Q115899.fctrCs
## 2.10906887 -0.03855171
## Q116881.fctrRight Q98197.fctrNo
## 1.17244801 -1.22486735
## Q98869.fctrNo Q99480.fctrNo
## -0.70827865 -0.47301091
## Hhold.fctrPKn:.clusterid.fctr2 Hhold.fctrMKy:.clusterid.fctr4
## -0.45120258 0.41134195
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.11820244
## [1] "max lambda < lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.32000305 0.20492060
## Hhold.fctrPKn Hhold.fctrPKy
## -0.76626059 -0.56953233
## Income.fctr.Q Income.fctr^6
## 0.23223093 -0.16935738
## Q100562.fctrNo Q101163.fctrDad
## -0.07488300 1.02453813
## Q106272.fctrNo Q106997.fctrGr
## -0.36297392 0.59530148
## Q108855.fctrYes! Q110740.fctrPC
## 0.36312816 0.54320298
## Q113181.fctrNo Q113181.fctrYes
## -1.53413324 0.76056725
## Q115611.fctrNo Q115611.fctrYes
## -0.74313134 2.16965670
## Q115899.fctrCs Q116881.fctrRight
## -0.12899583 1.25786772
## Q116953.fctrNo Q118232.fctrId
## 0.08674197 -0.04342297
## Q120472.fctrScience Q122120.fctrNo
## 0.08381197 -0.09058921
## Q98197.fctrNo Q98869.fctrNo
## -1.26517343 -0.78605038
## Q99480.fctrNo Q99716.fctrYes
## -0.54115654 -0.04440177
## Hhold.fctrPKn:.clusterid.fctr2 Hhold.fctrMKy:.clusterid.fctr4
## -0.53626183 0.48139180
## YOB.Age.fctr(35,40]:YOB.Age.dff
## -0.23115125
## [1] "myfit_mdl: train diagnostics complete: 50.299000 secs"
## Prediction
## Reference D R
## D 506 532
## R 397 1024
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 6.222041e-01 2.118076e-01 6.027014e-01 6.414154e-01 5.778772e-01
## AccuracyPValue McnemarPValue
## 4.234474e-06 1.100624e-05
## [1] "myfit_mdl: predict complete: 57.591000 secs"
## id
## 1 Final.All.X#expoTrans.spatialSign#rcv#glmnet
## feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 48.902 3.79
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.570395 0.2899807 0.8508093 0.6530192
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.55 0.6879409 0.5991618
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.6027014 0.6414154 0.1180901
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01413901 0.02885074
## [1] "myfit_mdl: exit: 57.616000 secs"rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 4 fit.data.training 2 0 0 600.612 658.729
## 5 fit.data.training 2 1 1 658.730 NA
## elapsed
## 4 58.117
## 5 NA#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glbMdlFinId)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glbMdlFinId)$feats, ","))
if (glb_is_classification)
# mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
# mdlEnsembleComps <- gsub(paste0("^",
# gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
# "", mdlEnsembleComps)
mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$prob %in% mdlEnsembleComps)] else
mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
mygetPredictIds(glb_rsp_var, thsMdlId)$value %in% mdlEnsembleComps)]
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
# glb_fin_mdl uses the same coefficients as glb_sel_mdl,
# so copy the "Final" columns into "non-Final" columns
glbObsTrn[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
glbObsTrn[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
glbObsNew[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
glbObsNew[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.45glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)## All.X.expoTrans.spatialSign.rcv.glmnet.imp
## Q115611.fctrYes 100.0000000
## Q113181.fctrNo 95.3534018
## Q98197.fctrNo 51.2895374
## Q116881.fctrRight 63.5102201
## Q101163.fctrDad 52.9462718
## Q113181.fctrYes 37.0837563
## Q98869.fctrNo 53.8392977
## Q115611.fctrNo 33.9313892
## Hhold.fctrPKn 72.3736413
## Q106997.fctrGr 53.8293468
## Q99480.fctrNo 54.7705038
## Hhold.fctrPKy 48.8973726
## Q110740.fctrPC 16.2478906
## Hhold.fctrPKn:.clusterid.fctr2 0.0000000
## Hhold.fctrMKy:.clusterid.fctr4 13.1648041
## Q106272.fctrNo 28.7696462
## Q108855.fctrYes! 1.0727486
## Hhold.fctrMKy 32.0285479
## Income.fctr.Q 18.7794022
## YOB.Age.fctr(35,40]:YOB.Age.dff 14.9297986
## Income.fctr^6 14.3529560
## Q115899.fctrCs 17.7016232
## Q122120.fctrNo 0.0000000
## Q116953.fctrNo 12.4240036
## Q120472.fctrScience 19.0457801
## Q100562.fctrNo 21.9532492
## Q99716.fctrYes 12.6078177
## Q118232.fctrId 0.0000000
## .rnorm 0.0000000
## Edn.fctr.C 0.0000000
## Edn.fctr.L 0.0000000
## Edn.fctr.Q 0.0000000
## Edn.fctr^4 0.0000000
## Edn.fctr^5 0.0000000
## Edn.fctr^6 0.0000000
## Edn.fctr^7 0.0000000
## Gender.fctrF 0.0000000
## Gender.fctrM 0.0000000
## Hhold.fctrMKn 0.0000000
## Hhold.fctrMKn:.clusterid.fctr2 0.0000000
## Hhold.fctrMKn:.clusterid.fctr3 0.0000000
## Hhold.fctrMKn:.clusterid.fctr4 0.0000000
## Hhold.fctrMKy:.clusterid.fctr2 0.0000000
## Hhold.fctrMKy:.clusterid.fctr3 0.0000000
## Hhold.fctrN:.clusterid.fctr2 0.0000000
## Hhold.fctrN:.clusterid.fctr3 0.0000000
## Hhold.fctrN:.clusterid.fctr4 13.2538905
## Hhold.fctrPKn:.clusterid.fctr3 0.0000000
## Hhold.fctrPKn:.clusterid.fctr4 0.0000000
## Hhold.fctrPKy:.clusterid.fctr2 0.0000000
## Hhold.fctrPKy:.clusterid.fctr3 0.0000000
## Hhold.fctrPKy:.clusterid.fctr4 24.0708883
## Hhold.fctrSKn 0.0000000
## Hhold.fctrSKn:.clusterid.fctr2 11.3977291
## Hhold.fctrSKn:.clusterid.fctr3 0.0000000
## Hhold.fctrSKn:.clusterid.fctr4 0.0000000
## Hhold.fctrSKy 14.5868679
## Hhold.fctrSKy:.clusterid.fctr2 0.0000000
## Hhold.fctrSKy:.clusterid.fctr3 13.9491938
## Hhold.fctrSKy:.clusterid.fctr4 0.0000000
## Income.fctr.C 23.6556411
## Income.fctr.L 0.0000000
## Income.fctr^4 0.0000000
## Income.fctr^5 0.0000000
## Q100010.fctrNo 0.0000000
## Q100010.fctrYes 0.0000000
## Q100562.fctrYes 0.0000000
## Q100680.fctrNo 0.0000000
## Q100680.fctrYes 0.0000000
## Q100689.fctrNo 0.0000000
## Q100689.fctrYes 0.1638523
## Q101162.fctrOptimist 0.0000000
## Q101162.fctrPessimist 0.0000000
## Q101163.fctrMom 0.0000000
## Q101596.fctrNo 0.0000000
## Q101596.fctrYes 0.0000000
## Q102089.fctrOwn 4.0312831
## Q102089.fctrRent 0.0000000
## Q102289.fctrNo 0.0000000
## Q102289.fctrYes 0.0000000
## Q102674.fctrNo 0.0000000
## Q102674.fctrYes 0.0000000
## Q102687.fctrNo 0.0000000
## Q102687.fctrYes 0.0000000
## Q102906.fctrNo 0.0000000
## Q102906.fctrYes 0.0000000
## Q103293.fctrNo 0.0000000
## Q103293.fctrYes 0.0000000
## Q104996.fctrNo 0.0000000
## Q104996.fctrYes 13.3267802
## Q105655.fctrNo 0.0000000
## Q105655.fctrYes 28.1499086
## Q105840.fctrNo 0.0000000
## Q105840.fctrYes 0.0000000
## Q106042.fctrNo 0.0000000
## Q106042.fctrYes 0.0000000
## Q106272.fctrYes 0.9188083
## Q106388.fctrNo 0.0000000
## Q106388.fctrYes 0.0000000
## Q106389.fctrNo 0.0000000
## Q106389.fctrYes 0.0000000
## Q106993.fctrNo 0.0000000
## Q106993.fctrYes 0.0000000
## Q106997.fctrYy 0.0000000
## Q107491.fctrNo 0.0000000
## Q107491.fctrYes 0.0000000
## Q107869.fctrNo 0.0000000
## Q107869.fctrYes 0.0000000
## Q108342.fctrIn-person 0.0000000
## Q108342.fctrOnline 0.0000000
## Q108343.fctrNo 0.0000000
## Q108343.fctrYes 0.0000000
## Q108617.fctrNo 0.0000000
## Q108617.fctrYes 0.0000000
## Q108754.fctrNo 0.0000000
## Q108754.fctrYes 0.0000000
## Q108855.fctrUmm... 14.1070607
## Q108856.fctrSocialize 0.0000000
## Q108856.fctrSpace 0.0000000
## Q108950.fctrCautious 0.0000000
## Q108950.fctrRisk-friendly 0.0000000
## Q109367.fctrNo 21.4635778
## Q109367.fctrYes 8.8146390
## Q110740.fctrMac 0.0000000
## Q111220.fctrNo 0.0000000
## Q111220.fctrYes 0.0000000
## Q111580.fctrDemanding 0.0000000
## Q111580.fctrSupportive 0.0000000
## Q111848.fctrNo 0.0000000
## Q111848.fctrYes 0.0000000
## Q112270.fctrNo 0.0000000
## Q112270.fctrYes 0.0000000
## Q112478.fctrNo 0.0000000
## Q112478.fctrYes 0.0000000
## Q112512.fctrNo 0.0000000
## Q112512.fctrYes 0.0000000
## Q113583.fctrTalk 0.0000000
## Q113583.fctrTunes 0.0000000
## Q113584.fctrPeople 0.0000000
## Q113584.fctrTechnology 0.0000000
## Q113992.fctrNo 0.0000000
## Q113992.fctrYes 0.0000000
## Q114152.fctrNo 0.0000000
## Q114152.fctrYes 0.0000000
## Q114386.fctrMysterious 7.6865479
## Q114386.fctrTMI 0.0000000
## Q114517.fctrNo 26.3106333
## Q114517.fctrYes 0.0000000
## Q114748.fctrNo 0.0000000
## Q114748.fctrYes 0.0000000
## Q114961.fctrNo 0.0000000
## Q114961.fctrYes 0.0000000
## Q115195.fctrNo 0.0000000
## Q115195.fctrYes 0.0000000
## Q115390.fctrNo 0.0000000
## Q115390.fctrYes 0.0000000
## Q115602.fctrNo 0.0000000
## Q115602.fctrYes 0.0000000
## Q115610.fctrNo 0.0000000
## Q115610.fctrYes 0.0000000
## Q115777.fctrEnd 0.0000000
## Q115777.fctrStart 0.0000000
## Q115899.fctrMe 0.0000000
## Q116197.fctrA.M. 0.0000000
## Q116197.fctrP.M. 0.0000000
## Q116441.fctrNo 0.0000000
## Q116441.fctrYes 0.0000000
## Q116448.fctrNo 0.0000000
## Q116448.fctrYes 0.0000000
## Q116601.fctrNo 5.7800906
## Q116601.fctrYes 0.0000000
## Q116797.fctrNo 0.0000000
## Q116797.fctrYes 0.0000000
## Q116881.fctrHappy 16.8262843
## Q116953.fctrYes 0.0000000
## Q117186.fctrCool headed 0.0000000
## Q117186.fctrHot headed 0.0000000
## Q117193.fctrOdd hours 0.0000000
## Q117193.fctrStandard hours 3.5026709
## Q118117.fctrNo 0.0000000
## Q118117.fctrYes 0.0000000
## Q118232.fctrPr 0.0000000
## Q118233.fctrNo 0.0000000
## Q118233.fctrYes 0.0000000
## Q118237.fctrNo 0.0000000
## Q118237.fctrYes 0.0000000
## Q118892.fctrNo 16.9276058
## Q118892.fctrYes 0.0000000
## Q119334.fctrNo 0.0000000
## Q119334.fctrYes 0.0000000
## Q119650.fctrGiving 35.7211724
## Q119650.fctrReceiving 0.0000000
## Q119851.fctrNo 0.0000000
## Q119851.fctrYes 0.0000000
## Q120012.fctrNo 0.0000000
## Q120012.fctrYes 0.0000000
## Q120014.fctrNo 0.0000000
## Q120014.fctrYes 0.0000000
## Q120194.fctrStudy first 0.0000000
## Q120194.fctrTry first 0.0000000
## Q120379.fctrNo 0.0000000
## Q120379.fctrYes 5.4427713
## Q120472.fctrArt 0.0000000
## Q120650.fctrNo 0.0000000
## Q120650.fctrYes 0.0000000
## Q120978.fctrNo 0.0000000
## Q120978.fctrYes 0.0000000
## Q121011.fctrNo 0.0000000
## Q121011.fctrYes 0.0000000
## Q121699.fctrNo 0.0000000
## Q121699.fctrYes 0.0000000
## Q121700.fctrNo 0.0000000
## Q121700.fctrYes 0.0000000
## Q122120.fctrYes 0.0000000
## Q122769.fctrNo 0.0000000
## Q122769.fctrYes 3.6547887
## Q122770.fctrNo 0.0000000
## Q122770.fctrYes 0.0000000
## Q122771.fctrPc 0.0000000
## Q122771.fctrPt 0.4572496
## Q123464.fctrNo 0.0000000
## Q123464.fctrYes 0.0000000
## Q123621.fctrNo 0.0000000
## Q123621.fctrYes 24.3374872
## Q124122.fctrNo 0.0000000
## Q124122.fctrYes 0.0000000
## Q124742.fctrNo 33.9726792
## Q124742.fctrYes 0.0000000
## Q96024.fctrNo 1.1407544
## Q96024.fctrYes 0.0000000
## Q98059.fctrOnly-child 0.0000000
## Q98059.fctrYes 0.0000000
## Q98078.fctrNo 0.0000000
## Q98078.fctrYes 0.0000000
## Q98197.fctrYes 0.0000000
## Q98578.fctrNo 0.0000000
## Q98578.fctrYes 0.0000000
## Q98869.fctrYes 0.0000000
## Q99480.fctrYes 0.1700106
## Q99581.fctrNo 0.0000000
## Q99581.fctrYes 0.0000000
## Q99716.fctrNo 0.0000000
## Q99982.fctrCheck! 0.0000000
## Q99982.fctrNope 0.0000000
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.6262704
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.1756714
## YOB.Age.fctr.C 0.0000000
## YOB.Age.fctr.L 0.0000000
## YOB.Age.fctr.Q 0.0000000
## YOB.Age.fctrNA:YOB.Age.dff 0.0000000
## YOB.Age.fctr^4 0.0000000
## YOB.Age.fctr^5 0.0000000
## YOB.Age.fctr^6 0.0000000
## YOB.Age.fctr^7 0.0000000
## YOB.Age.fctr^8 0.0000000
## Final.All.X.expoTrans.spatialSign.rcv.glmnet.imp
## Q115611.fctrYes 100.0000000
## Q113181.fctrNo 70.4599840
## Q98197.fctrNo 58.1928167
## Q116881.fctrRight 56.7693170
## Q101163.fctrDad 46.0785490
## Q113181.fctrYes 35.7542760
## Q98869.fctrNo 34.8905841
## Q115611.fctrNo 34.2948032
## Hhold.fctrPKn 34.2595973
## Q106997.fctrGr 24.2823620
## Q99480.fctrNo 23.6702142
## Hhold.fctrPKy 23.4735931
## Q110740.fctrPC 23.1090302
## Hhold.fctrPKn:.clusterid.fctr2 23.0357267
## Hhold.fctrMKy:.clusterid.fctr4 20.8299540
## Q106272.fctrNo 15.1215088
## Q108855.fctrYes! 14.9203118
## Hhold.fctrMKy 8.7552062
## Income.fctr.Q 8.5575899
## YOB.Age.fctr(35,40]:YOB.Age.dff 8.0999511
## Income.fctr^6 5.5666257
## Q115899.fctrCs 3.8628641
## Q122120.fctrNo 2.0634941
## Q116953.fctrNo 1.9758594
## Q120472.fctrScience 1.9091181
## Q100562.fctrNo 1.7057288
## Q99716.fctrYes 1.0114096
## Q118232.fctrId 0.9891138
## .rnorm 0.0000000
## Edn.fctr.C 0.0000000
## Edn.fctr.L 0.0000000
## Edn.fctr.Q 0.0000000
## Edn.fctr^4 0.0000000
## Edn.fctr^5 0.0000000
## Edn.fctr^6 0.0000000
## Edn.fctr^7 0.0000000
## Gender.fctrF 0.0000000
## Gender.fctrM 0.0000000
## Hhold.fctrMKn 0.0000000
## Hhold.fctrMKn:.clusterid.fctr2 0.0000000
## Hhold.fctrMKn:.clusterid.fctr3 0.0000000
## Hhold.fctrMKn:.clusterid.fctr4 0.0000000
## Hhold.fctrMKy:.clusterid.fctr2 0.0000000
## Hhold.fctrMKy:.clusterid.fctr3 0.0000000
## Hhold.fctrN:.clusterid.fctr2 0.0000000
## Hhold.fctrN:.clusterid.fctr3 0.0000000
## Hhold.fctrN:.clusterid.fctr4 0.0000000
## Hhold.fctrPKn:.clusterid.fctr3 0.0000000
## Hhold.fctrPKn:.clusterid.fctr4 0.0000000
## Hhold.fctrPKy:.clusterid.fctr2 0.0000000
## Hhold.fctrPKy:.clusterid.fctr3 0.0000000
## Hhold.fctrPKy:.clusterid.fctr4 0.0000000
## Hhold.fctrSKn 0.0000000
## Hhold.fctrSKn:.clusterid.fctr2 0.0000000
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## Hhold.fctrSKy 0.0000000
## Hhold.fctrSKy:.clusterid.fctr2 0.0000000
## Hhold.fctrSKy:.clusterid.fctr3 0.0000000
## Hhold.fctrSKy:.clusterid.fctr4 0.0000000
## Income.fctr.C 0.0000000
## Income.fctr.L 0.0000000
## Income.fctr^4 0.0000000
## Income.fctr^5 0.0000000
## Q100010.fctrNo 0.0000000
## Q100010.fctrYes 0.0000000
## Q100562.fctrYes 0.0000000
## Q100680.fctrNo 0.0000000
## Q100680.fctrYes 0.0000000
## Q100689.fctrNo 0.0000000
## Q100689.fctrYes 0.0000000
## Q101162.fctrOptimist 0.0000000
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## Q101163.fctrMom 0.0000000
## Q101596.fctrNo 0.0000000
## Q101596.fctrYes 0.0000000
## Q102089.fctrOwn 0.0000000
## Q102089.fctrRent 0.0000000
## Q102289.fctrNo 0.0000000
## Q102289.fctrYes 0.0000000
## Q102674.fctrNo 0.0000000
## Q102674.fctrYes 0.0000000
## Q102687.fctrNo 0.0000000
## Q102687.fctrYes 0.0000000
## Q102906.fctrNo 0.0000000
## Q102906.fctrYes 0.0000000
## Q103293.fctrNo 0.0000000
## Q103293.fctrYes 0.0000000
## Q104996.fctrNo 0.0000000
## Q104996.fctrYes 0.0000000
## Q105655.fctrNo 0.0000000
## Q105655.fctrYes 0.0000000
## Q105840.fctrNo 0.0000000
## Q105840.fctrYes 0.0000000
## Q106042.fctrNo 0.0000000
## Q106042.fctrYes 0.0000000
## Q106272.fctrYes 0.0000000
## Q106388.fctrNo 0.0000000
## Q106388.fctrYes 0.0000000
## Q106389.fctrNo 0.0000000
## Q106389.fctrYes 0.0000000
## Q106993.fctrNo 0.0000000
## Q106993.fctrYes 0.0000000
## Q106997.fctrYy 0.0000000
## Q107491.fctrNo 0.0000000
## Q107491.fctrYes 0.0000000
## Q107869.fctrNo 0.0000000
## Q107869.fctrYes 0.0000000
## Q108342.fctrIn-person 0.0000000
## Q108342.fctrOnline 0.0000000
## Q108343.fctrNo 0.0000000
## Q108343.fctrYes 0.0000000
## Q108617.fctrNo 0.0000000
## Q108617.fctrYes 0.0000000
## Q108754.fctrNo 0.0000000
## Q108754.fctrYes 0.0000000
## Q108855.fctrUmm... 0.0000000
## Q108856.fctrSocialize 0.0000000
## Q108856.fctrSpace 0.0000000
## Q108950.fctrCautious 0.0000000
## Q108950.fctrRisk-friendly 0.0000000
## Q109367.fctrNo 0.0000000
## Q109367.fctrYes 0.0000000
## Q110740.fctrMac 0.0000000
## Q111220.fctrNo 0.0000000
## Q111220.fctrYes 0.0000000
## Q111580.fctrDemanding 0.0000000
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## Q112270.fctrNo 0.0000000
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## Q112478.fctrNo 0.0000000
## Q112478.fctrYes 0.0000000
## Q112512.fctrNo 0.0000000
## Q112512.fctrYes 0.0000000
## Q113583.fctrTalk 0.0000000
## Q113583.fctrTunes 0.0000000
## Q113584.fctrPeople 0.0000000
## Q113584.fctrTechnology 0.0000000
## Q113992.fctrNo 0.0000000
## Q113992.fctrYes 0.0000000
## Q114152.fctrNo 0.0000000
## Q114152.fctrYes 0.0000000
## Q114386.fctrMysterious 0.0000000
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## Q114517.fctrNo 0.0000000
## Q114517.fctrYes 0.0000000
## Q114748.fctrNo 0.0000000
## Q114748.fctrYes 0.0000000
## Q114961.fctrNo 0.0000000
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## Q115195.fctrNo 0.0000000
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## Q115390.fctrNo 0.0000000
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## Q115610.fctrNo 0.0000000
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## Q115777.fctrEnd 0.0000000
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## Q116448.fctrNo 0.0000000
## Q116448.fctrYes 0.0000000
## Q116601.fctrNo 0.0000000
## Q116601.fctrYes 0.0000000
## Q116797.fctrNo 0.0000000
## Q116797.fctrYes 0.0000000
## Q116881.fctrHappy 0.0000000
## Q116953.fctrYes 0.0000000
## Q117186.fctrCool headed 0.0000000
## Q117186.fctrHot headed 0.0000000
## Q117193.fctrOdd hours 0.0000000
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## Q118232.fctrPr 0.0000000
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## Q118237.fctrYes 0.0000000
## Q118892.fctrNo 0.0000000
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## Q119334.fctrNo 0.0000000
## Q119334.fctrYes 0.0000000
## Q119650.fctrGiving 0.0000000
## Q119650.fctrReceiving 0.0000000
## Q119851.fctrNo 0.0000000
## Q119851.fctrYes 0.0000000
## Q120012.fctrNo 0.0000000
## Q120012.fctrYes 0.0000000
## Q120014.fctrNo 0.0000000
## Q120014.fctrYes 0.0000000
## Q120194.fctrStudy first 0.0000000
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## Q120379.fctrNo 0.0000000
## Q120379.fctrYes 0.0000000
## Q120472.fctrArt 0.0000000
## Q120650.fctrNo 0.0000000
## Q120650.fctrYes 0.0000000
## Q120978.fctrNo 0.0000000
## Q120978.fctrYes 0.0000000
## Q121011.fctrNo 0.0000000
## Q121011.fctrYes 0.0000000
## Q121699.fctrNo 0.0000000
## Q121699.fctrYes 0.0000000
## Q121700.fctrNo 0.0000000
## Q121700.fctrYes 0.0000000
## Q122120.fctrYes 0.0000000
## Q122769.fctrNo 0.0000000
## Q122769.fctrYes 0.0000000
## Q122770.fctrNo 0.0000000
## Q122770.fctrYes 0.0000000
## Q122771.fctrPc 0.0000000
## Q122771.fctrPt 0.0000000
## Q123464.fctrNo 0.0000000
## Q123464.fctrYes 0.0000000
## Q123621.fctrNo 0.0000000
## Q123621.fctrYes 0.0000000
## Q124122.fctrNo 0.0000000
## Q124122.fctrYes 0.0000000
## Q124742.fctrNo 0.0000000
## Q124742.fctrYes 0.0000000
## Q96024.fctrNo 0.0000000
## Q96024.fctrYes 0.0000000
## Q98059.fctrOnly-child 0.0000000
## Q98059.fctrYes 0.0000000
## Q98078.fctrNo 0.0000000
## Q98078.fctrYes 0.0000000
## Q98197.fctrYes 0.0000000
## Q98578.fctrNo 0.0000000
## Q98578.fctrYes 0.0000000
## Q98869.fctrYes 0.0000000
## Q99480.fctrYes 0.0000000
## Q99581.fctrNo 0.0000000
## Q99581.fctrYes 0.0000000
## Q99716.fctrNo 0.0000000
## Q99982.fctrCheck! 0.0000000
## Q99982.fctrNope 0.0000000
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.0000000
## YOB.Age.fctr.C 0.0000000
## YOB.Age.fctr.L 0.0000000
## YOB.Age.fctr.Q 0.0000000
## YOB.Age.fctrNA:YOB.Age.dff 0.0000000
## YOB.Age.fctr^4 0.0000000
## YOB.Age.fctr^5 0.0000000
## YOB.Age.fctr^6 0.0000000
## YOB.Age.fctr^7 0.0000000
## YOB.Age.fctr^8 0.0000000
## imp
## Q115611.fctrYes 100.0000000
## Q113181.fctrNo 70.4599840
## Q98197.fctrNo 58.1928167
## Q116881.fctrRight 56.7693170
## Q101163.fctrDad 46.0785490
## Q113181.fctrYes 35.7542760
## Q98869.fctrNo 34.8905841
## Q115611.fctrNo 34.2948032
## Hhold.fctrPKn 34.2595973
## Q106997.fctrGr 24.2823620
## Q99480.fctrNo 23.6702142
## Hhold.fctrPKy 23.4735931
## Q110740.fctrPC 23.1090302
## Hhold.fctrPKn:.clusterid.fctr2 23.0357267
## Hhold.fctrMKy:.clusterid.fctr4 20.8299540
## Q106272.fctrNo 15.1215088
## Q108855.fctrYes! 14.9203118
## Hhold.fctrMKy 8.7552062
## Income.fctr.Q 8.5575899
## YOB.Age.fctr(35,40]:YOB.Age.dff 8.0999511
## Income.fctr^6 5.5666257
## Q115899.fctrCs 3.8628641
## Q122120.fctrNo 2.0634941
## Q116953.fctrNo 1.9758594
## Q120472.fctrScience 1.9091181
## Q100562.fctrNo 1.7057288
## Q99716.fctrYes 1.0114096
## Q118232.fctrId 0.9891138
## .rnorm 0.0000000
## Edn.fctr.C 0.0000000
## Edn.fctr.L 0.0000000
## Edn.fctr.Q 0.0000000
## Edn.fctr^4 0.0000000
## Edn.fctr^5 0.0000000
## Edn.fctr^6 0.0000000
## Edn.fctr^7 0.0000000
## Gender.fctrF 0.0000000
## Gender.fctrM 0.0000000
## Hhold.fctrMKn 0.0000000
## Hhold.fctrMKn:.clusterid.fctr2 0.0000000
## Hhold.fctrMKn:.clusterid.fctr3 0.0000000
## Hhold.fctrMKn:.clusterid.fctr4 0.0000000
## Hhold.fctrMKy:.clusterid.fctr2 0.0000000
## Hhold.fctrMKy:.clusterid.fctr3 0.0000000
## Hhold.fctrN:.clusterid.fctr2 0.0000000
## Hhold.fctrN:.clusterid.fctr3 0.0000000
## Hhold.fctrN:.clusterid.fctr4 0.0000000
## Hhold.fctrPKn:.clusterid.fctr3 0.0000000
## Hhold.fctrPKn:.clusterid.fctr4 0.0000000
## Hhold.fctrPKy:.clusterid.fctr2 0.0000000
## Hhold.fctrPKy:.clusterid.fctr3 0.0000000
## Hhold.fctrPKy:.clusterid.fctr4 0.0000000
## Hhold.fctrSKn 0.0000000
## Hhold.fctrSKn:.clusterid.fctr2 0.0000000
## Hhold.fctrSKn:.clusterid.fctr3 0.0000000
## Hhold.fctrSKn:.clusterid.fctr4 0.0000000
## Hhold.fctrSKy 0.0000000
## Hhold.fctrSKy:.clusterid.fctr2 0.0000000
## Hhold.fctrSKy:.clusterid.fctr3 0.0000000
## Hhold.fctrSKy:.clusterid.fctr4 0.0000000
## Income.fctr.C 0.0000000
## Income.fctr.L 0.0000000
## Income.fctr^4 0.0000000
## Income.fctr^5 0.0000000
## Q100010.fctrNo 0.0000000
## Q100010.fctrYes 0.0000000
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## Q100680.fctrYes 0.0000000
## Q100689.fctrNo 0.0000000
## Q100689.fctrYes 0.0000000
## Q101162.fctrOptimist 0.0000000
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## Q101596.fctrYes 0.0000000
## Q102089.fctrOwn 0.0000000
## Q102089.fctrRent 0.0000000
## Q102289.fctrNo 0.0000000
## Q102289.fctrYes 0.0000000
## Q102674.fctrNo 0.0000000
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## Q102906.fctrNo 0.0000000
## Q102906.fctrYes 0.0000000
## Q103293.fctrNo 0.0000000
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## Q104996.fctrNo 0.0000000
## Q104996.fctrYes 0.0000000
## Q105655.fctrNo 0.0000000
## Q105655.fctrYes 0.0000000
## Q105840.fctrNo 0.0000000
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## Q106042.fctrNo 0.0000000
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## Q106272.fctrYes 0.0000000
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## Q107491.fctrYes 0.0000000
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## Q108342.fctrIn-person 0.0000000
## Q108342.fctrOnline 0.0000000
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## Q111580.fctrDemanding 0.0000000
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## Q112512.fctrNo 0.0000000
## Q112512.fctrYes 0.0000000
## Q113583.fctrTalk 0.0000000
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## Q114386.fctrMysterious 0.0000000
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## Q119650.fctrGiving 0.0000000
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## Q121700.fctrYes 0.0000000
## Q122120.fctrYes 0.0000000
## Q122769.fctrNo 0.0000000
## Q122769.fctrYes 0.0000000
## Q122770.fctrNo 0.0000000
## Q122770.fctrYes 0.0000000
## Q122771.fctrPc 0.0000000
## Q122771.fctrPt 0.0000000
## Q123464.fctrNo 0.0000000
## Q123464.fctrYes 0.0000000
## Q123621.fctrNo 0.0000000
## Q123621.fctrYes 0.0000000
## Q124122.fctrNo 0.0000000
## Q124122.fctrYes 0.0000000
## Q124742.fctrNo 0.0000000
## Q124742.fctrYes 0.0000000
## Q96024.fctrNo 0.0000000
## Q96024.fctrYes 0.0000000
## Q98059.fctrOnly-child 0.0000000
## Q98059.fctrYes 0.0000000
## Q98078.fctrNo 0.0000000
## Q98078.fctrYes 0.0000000
## Q98197.fctrYes 0.0000000
## Q98578.fctrNo 0.0000000
## Q98578.fctrYes 0.0000000
## Q98869.fctrYes 0.0000000
## Q99480.fctrYes 0.0000000
## Q99581.fctrNo 0.0000000
## Q99581.fctrYes 0.0000000
## Q99716.fctrNo 0.0000000
## Q99982.fctrCheck! 0.0000000
## Q99982.fctrNope 0.0000000
## YOB.Age.fctr(15,20]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(20,25]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(25,30]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(30,35]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(40,50]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(50,65]:YOB.Age.dff 0.0000000
## YOB.Age.fctr(65,90]:YOB.Age.dff 0.0000000
## YOB.Age.fctr.C 0.0000000
## YOB.Age.fctr.L 0.0000000
## YOB.Age.fctr.Q 0.0000000
## YOB.Age.fctrNA:YOB.Age.dff 0.0000000
## YOB.Age.fctr^4 0.0000000
## YOB.Age.fctr^5 0.0000000
## YOB.Age.fctr^6 0.0000000
## YOB.Age.fctr^7 0.0000000
## YOB.Age.fctr^8 0.0000000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId,
prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId) ## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr
## 1 26 R
## 2 5879 R
## 3 599 R
## 4 660 R
## 5 470 R
## 6 403 R
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.prob
## 1 0.2863553
## 2 0.3031190
## 3 0.3348274
## 4 0.3788744
## 5 NA
## 6 0.3401278
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet
## 1 D
## 2 D
## 3 D
## 4 D
## 5 <NA>
## 6 D
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.err
## 1 TRUE
## 2 TRUE
## 3 TRUE
## 4 TRUE
## 5 NA
## 6 TRUE
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.err.abs
## 1 0.7136447
## 2 0.6968810
## 3 0.6651726
## 4 0.6211256
## 5 NA
## 6 0.6598722
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 NA
## 6 FALSE
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.prob
## 1 0.3881989
## 2 0.3956293
## 3 0.3965126
## 4 0.4034179
## 5 0.4035234
## 6 0.4050048
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet
## 1 D
## 2 D
## 3 D
## 4 D
## 5 D
## 6 D
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.err
## 1 TRUE
## 2 TRUE
## 3 TRUE
## 4 TRUE
## 5 TRUE
## 6 TRUE
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.err.abs
## 1 0.6118011
## 2 0.6043707
## 3 0.6034874
## 4 0.5965821
## 5 0.5964766
## 6 0.5949952
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.accurate
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.error
## 1 -0.06180115
## 2 -0.05437067
## 3 -0.05348737
## 4 -0.04658208
## 5 -0.04647663
## 6 -0.04499521
## USER_ID Party.fctr
## 22 2169 R
## 41 1343 R
## 479 1799 D
## 509 1485 D
## 575 5837 D
## 984 5544 D
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.prob
## 22 0.4396815
## 41 NA
## 479 0.5609044
## 509 0.5570340
## 575 0.5600928
## 984 0.7386573
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet
## 22 D
## 41 <NA>
## 479 R
## 509 R
## 575 R
## 984 R
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.err
## 22 TRUE
## 41 NA
## 479 TRUE
## 509 TRUE
## 575 TRUE
## 984 TRUE
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.err.abs
## 22 0.5603185
## 41 NA
## 479 0.5609044
## 509 0.5570340
## 575 0.5600928
## 984 0.7386573
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.is.acc
## 22 FALSE
## 41 NA
## 479 FALSE
## 509 FALSE
## 575 FALSE
## 984 FALSE
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.prob
## 22 0.4349910
## 41 0.4464444
## 479 0.5517721
## 509 0.5576558
## 575 0.5676140
## 984 0.7222320
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet
## 22 D
## 41 D
## 479 R
## 509 R
## 575 R
## 984 R
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.err
## 22 TRUE
## 41 TRUE
## 479 TRUE
## 509 TRUE
## 575 TRUE
## 984 TRUE
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.err.abs
## 22 0.5650090
## 41 0.5535556
## 479 0.5517721
## 509 0.5576558
## 575 0.5676140
## 984 0.7222320
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.is.acc
## 22 FALSE
## 41 FALSE
## 479 FALSE
## 509 FALSE
## 575 FALSE
## 984 FALSE
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.accurate
## 22 FALSE
## 41 FALSE
## 479 FALSE
## 509 FALSE
## 575 FALSE
## 984 FALSE
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.error
## 22 -0.015008966
## 41 -0.003555578
## 479 0.101772082
## 509 0.107655765
## 575 0.117614005
## 984 0.272231965
## USER_ID Party.fctr
## 995 3474 D
## 996 2641 D
## 997 1311 D
## 998 78 D
## 999 3578 D
## 1000 1309 D
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.prob
## 995 NA
## 996 0.7627772
## 997 NA
## 998 0.7526049
## 999 0.7581158
## 1000 0.7703249
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet
## 995 <NA>
## 996 R
## 997 <NA>
## 998 R
## 999 R
## 1000 R
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.err
## 995 NA
## 996 TRUE
## 997 NA
## 998 TRUE
## 999 TRUE
## 1000 TRUE
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.err.abs
## 995 NA
## 996 0.7627772
## 997 NA
## 998 0.7526049
## 999 0.7581158
## 1000 0.7703249
## Party.fctr.All.X.expoTrans.spatialSign.rcv.glmnet.is.acc
## 995 NA
## 996 FALSE
## 997 NA
## 998 FALSE
## 999 FALSE
## 1000 FALSE
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.prob
## 995 0.7368335
## 996 0.7376522
## 997 0.7393650
## 998 0.7393765
## 999 0.7424068
## 1000 0.7520389
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet
## 995 R
## 996 R
## 997 R
## 998 R
## 999 R
## 1000 R
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.err
## 995 TRUE
## 996 TRUE
## 997 TRUE
## 998 TRUE
## 999 TRUE
## 1000 TRUE
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.err.abs
## 995 0.7368335
## 996 0.7376522
## 997 0.7393650
## 998 0.7393765
## 999 0.7424068
## 1000 0.7520389
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.is.acc
## 995 FALSE
## 996 FALSE
## 997 FALSE
## 998 FALSE
## 999 FALSE
## 1000 FALSE
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.accurate
## 995 FALSE
## 996 FALSE
## 997 FALSE
## 998 FALSE
## 999 FALSE
## 1000 FALSE
## Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.error
## 995 0.2868335
## 996 0.2876522
## 997 0.2893650
## 998 0.2893765
## 999 0.2924068
## 1000 0.3020389
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))## [1] "Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.prob"
## [2] "Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet"
## [3] "Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.err"
## [4] "Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.err.abs"
## [5] "Party.fctr.Final.All.X.expoTrans.spatialSign.rcv.glmnet.is.acc"for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))## character(0)print(setdiff(names(glbObsOOB), names(glbObsAll)))## character(0)for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.training.all.prediction
## 2.0000 5 2 0 0 1## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans =
## (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Transition:
## model.final not enabled; adding missing token(s)## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans
## = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Place:
## fit.data.training.all: added 1 missing token## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: model.final
## 3.0000 4 2 0 1 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 5 fit.data.training 2 1 1 658.730 669.282
## 6 predict.data.new 3 0 0 669.282 NA
## elapsed
## 5 10.552
## 6 NAStep 3.0: predict data new
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.45
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.45## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108## Warning: Removed 622 rows containing missing values (geom_point).
## Warning: Removed 622 rows containing missing values (geom_point).
## Warning: Removed 622 rows containing missing values (geom_point).
## Warning: Removed 622 rows containing missing values (geom_point).
## Warning: Removed 622 rows containing missing values (geom_point).
## Warning: Removed 622 rows containing missing values (geom_point).
## Warning: Removed 622 rows containing missing values (geom_point).
## Warning: Removed 622 rows containing missing values (geom_point).
## Warning: Removed 622 rows containing missing values (geom_point).
## Warning: Removed 622 rows containing missing values (geom_point).
## NULL## Loading required package: tidyr##
## Attaching package: 'tidyr'## The following object is masked from 'package:Matrix':
##
## expand## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"## [1] "Stacking file Votes_Ensemble_cnk06_out_fin.csv to prediction outputs..."## [1] 0.45## [1] "glbMdlSelId: All.X#expoTrans.spatialSign#rcv#glmnet"## [1] "glbMdlFinId: Final.All.X#expoTrans.spatialSign#rcv#glmnet"## [1] "Cross Validation issues:"
## MFO###myMFO_classfr Random###myrandom_classfr
## 0 0
## Max.cor.Y.rcv.1X1###glmnet
## 0## max.Accuracy.OOB
## All.X#expoTrans.spatialSign#rcv#glmnet 0.5936255
## All.X#spatialSign#rcv#glmnet 0.5936255
## All.X#expoTrans#rcv#glmnet 0.5916335
## All.X##rcv#glmnet 0.5896414
## All.X#YeoJohnson#rcv#glmnet 0.5876494
## All.X#conditionalX#rcv#glmnet 0.5856574
## Low.cor.X##rcv#glmnet 0.5856574
## All.X#scale#rcv#glmnet 0.5856574
## All.X#zv#rcv#glmnet 0.5856574
## All.X#range#rcv#glmnet 0.5856574
## All.X#center#rcv#glmnet 0.5856574
## All.X#center.scale#rcv#glmnet 0.5856574
## All.X#BoxCox#rcv#glmnet 0.5856574
## All.X#nzv#rcv#glmnet 0.5796813
## All.X#zv.pca#rcv#glmnet 0.5796813
## Max.cor.Y.rcv.1X1###glmnet 0.5756972
## Max.cor.Y##rcv#rpart 0.5737052
## All.X#ica#rcv#glmnet 0.5737052
## Random###myrandom_classfr 0.5737052
## MFO###myMFO_classfr 0.5737052
## Final.All.X#expoTrans.spatialSign#rcv#glmnet NA
## max.AUCROCR.OOB
## All.X#expoTrans.spatialSign#rcv#glmnet 0.5756912
## All.X#spatialSign#rcv#glmnet 0.5704991
## All.X#expoTrans#rcv#glmnet 0.5697040
## All.X##rcv#glmnet 0.5814512
## All.X#YeoJohnson#rcv#glmnet 0.5696554
## All.X#conditionalX#rcv#glmnet 0.5688766
## Low.cor.X##rcv#glmnet 0.5688766
## All.X#scale#rcv#glmnet 0.5688766
## All.X#zv#rcv#glmnet 0.5688766
## All.X#range#rcv#glmnet 0.5688766
## All.X#center#rcv#glmnet 0.5688766
## All.X#center.scale#rcv#glmnet 0.5688766
## All.X#BoxCox#rcv#glmnet 0.5688766
## All.X#nzv#rcv#glmnet 0.5784657
## All.X#zv.pca#rcv#glmnet 0.5588331
## Max.cor.Y.rcv.1X1###glmnet 0.5719513
## Max.cor.Y##rcv#rpart 0.5452362
## All.X#ica#rcv#glmnet 0.5297573
## Random###myrandom_classfr 0.5181075
## MFO###myMFO_classfr 0.5000000
## Final.All.X#expoTrans.spatialSign#rcv#glmnet NA
## max.AUCpROC.OOB
## All.X#expoTrans.spatialSign#rcv#glmnet 0.5600500
## All.X#spatialSign#rcv#glmnet 0.5554420
## All.X#expoTrans#rcv#glmnet 0.5554420
## All.X##rcv#glmnet 0.5541115
## All.X#YeoJohnson#rcv#glmnet 0.5554420
## All.X#conditionalX#rcv#glmnet 0.5554420
## Low.cor.X##rcv#glmnet 0.5554420
## All.X#scale#rcv#glmnet 0.5554420
## All.X#zv#rcv#glmnet 0.5554420
## All.X#range#rcv#glmnet 0.5554420
## All.X#center#rcv#glmnet 0.5554420
## All.X#center.scale#rcv#glmnet 0.5554420
## All.X#BoxCox#rcv#glmnet 0.5554420
## All.X#nzv#rcv#glmnet 0.5436299
## All.X#zv.pca#rcv#glmnet 0.5017848
## Max.cor.Y.rcv.1X1###glmnet 0.5468912
## Max.cor.Y##rcv#rpart 0.5468912
## All.X#ica#rcv#glmnet 0.4820223
## Random###myrandom_classfr 0.4990752
## MFO###myMFO_classfr 0.5000000
## Final.All.X#expoTrans.spatialSign#rcv#glmnet NA
## min.elapsedtime.everything
## All.X#expoTrans.spatialSign#rcv#glmnet 42.917
## All.X#spatialSign#rcv#glmnet 20.544
## All.X#expoTrans#rcv#glmnet 51.149
## All.X##rcv#glmnet 13.519
## All.X#YeoJohnson#rcv#glmnet 45.222
## All.X#conditionalX#rcv#glmnet 13.941
## Low.cor.X##rcv#glmnet 14.590
## All.X#scale#rcv#glmnet 14.819
## All.X#zv#rcv#glmnet 14.938
## All.X#range#rcv#glmnet 15.454
## All.X#center#rcv#glmnet 15.479
## All.X#center.scale#rcv#glmnet 16.964
## All.X#BoxCox#rcv#glmnet 17.604
## All.X#nzv#rcv#glmnet 17.988
## All.X#zv.pca#rcv#glmnet 42.121
## Max.cor.Y.rcv.1X1###glmnet 0.766
## Max.cor.Y##rcv#rpart 1.468
## All.X#ica#rcv#glmnet 22.835
## Random###myrandom_classfr 0.272
## MFO###myMFO_classfr 0.501
## Final.All.X#expoTrans.spatialSign#rcv#glmnet 48.902
## max.Accuracy.fit
## All.X#expoTrans.spatialSign#rcv#glmnet 0.6128471
## All.X#spatialSign#rcv#glmnet 0.6118257
## All.X#expoTrans#rcv#glmnet 0.6109764
## All.X##rcv#glmnet 0.6126764
## All.X#YeoJohnson#rcv#glmnet 0.6109767
## All.X#conditionalX#rcv#glmnet 0.6128487
## Low.cor.X##rcv#glmnet 0.6128487
## All.X#scale#rcv#glmnet 0.6128487
## All.X#zv#rcv#glmnet 0.6128487
## All.X#range#rcv#glmnet 0.6128487
## All.X#center#rcv#glmnet 0.6128487
## All.X#center.scale#rcv#glmnet 0.6128487
## All.X#BoxCox#rcv#glmnet 0.6128487
## All.X#nzv#rcv#glmnet 0.6087590
## All.X#zv.pca#rcv#glmnet 0.5959830
## Max.cor.Y.rcv.1X1###glmnet 0.6152274
## Max.cor.Y##rcv#rpart 0.6136958
## All.X#ica#rcv#glmnet 0.5918896
## Random###myrandom_classfr 0.5789474
## MFO###myMFO_classfr 0.5789474
## Final.All.X#expoTrans.spatialSign#rcv#glmnet 0.5991618
## opt.prob.threshold.fit
## All.X#expoTrans.spatialSign#rcv#glmnet 0.55
## All.X#spatialSign#rcv#glmnet 0.55
## All.X#expoTrans#rcv#glmnet 0.55
## All.X##rcv#glmnet 0.55
## All.X#YeoJohnson#rcv#glmnet 0.50
## All.X#conditionalX#rcv#glmnet 0.55
## Low.cor.X##rcv#glmnet 0.55
## All.X#scale#rcv#glmnet 0.55
## All.X#zv#rcv#glmnet 0.55
## All.X#range#rcv#glmnet 0.55
## All.X#center#rcv#glmnet 0.55
## All.X#center.scale#rcv#glmnet 0.55
## All.X#BoxCox#rcv#glmnet 0.55
## All.X#nzv#rcv#glmnet 0.55
## All.X#zv.pca#rcv#glmnet 0.55
## Max.cor.Y.rcv.1X1###glmnet 0.50
## Max.cor.Y##rcv#rpart 0.50
## All.X#ica#rcv#glmnet 0.50
## Random###myrandom_classfr 0.40
## MFO###myMFO_classfr 0.40
## Final.All.X#expoTrans.spatialSign#rcv#glmnet 0.55
## opt.prob.threshold.OOB
## All.X#expoTrans.spatialSign#rcv#glmnet 0.45
## All.X#spatialSign#rcv#glmnet 0.45
## All.X#expoTrans#rcv#glmnet 0.45
## All.X##rcv#glmnet 0.50
## All.X#YeoJohnson#rcv#glmnet 0.45
## All.X#conditionalX#rcv#glmnet 0.50
## Low.cor.X##rcv#glmnet 0.50
## All.X#scale#rcv#glmnet 0.50
## All.X#zv#rcv#glmnet 0.50
## All.X#range#rcv#glmnet 0.50
## All.X#center#rcv#glmnet 0.50
## All.X#center.scale#rcv#glmnet 0.50
## All.X#BoxCox#rcv#glmnet 0.50
## All.X#nzv#rcv#glmnet 0.50
## All.X#zv.pca#rcv#glmnet 0.55
## Max.cor.Y.rcv.1X1###glmnet 0.55
## Max.cor.Y##rcv#rpart 0.40
## All.X#ica#rcv#glmnet 0.40
## Random###myrandom_classfr 0.40
## MFO###myMFO_classfr 0.40
## Final.All.X#expoTrans.spatialSign#rcv#glmnet NA## [1] "All.X#expoTrans.spatialSign#rcv#glmnet OOB confusion matrix & accuracy: "
## Prediction
## Reference D R
## D 37 177
## R 27 261## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## PKn 22.893872 7.134215 30.83944 NA
## PKy 6.299374 2.971012 10.01024 NA
## SKy 28.827288 11.348368 40.69325 NA
## SKn 373.399097 97.262331 479.50176 NA
## MKy 300.522687 73.031659 380.12806 NA
## MKn 103.615614 34.933708 140.09919 NA
## N 53.063882 12.946810 67.03962 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.New.R
## PKn 0.025549310 0.02589641 0.024115756 50 2 13
## PKy 0.007153807 0.01195219 0.009646302 14 3 3
## SKy 0.031170158 0.04581673 0.043408360 61 2 25
## SKn 0.413898825 0.40039841 0.405144695 810 19 233
## MKy 0.347470618 0.31075697 0.313504823 680 2 193
## MKn 0.115482882 0.14940239 0.151125402 226 4 90
## N 0.059274400 0.05577689 0.053054662 116 1 32
## .n.OOB .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## PKn 13 40 23 15 50 15 63 0.5487857
## PKy 6 13 7 6 14 6 20 0.4951687
## SKy 23 43 41 27 61 27 84 0.4934073
## SKn 201 454 557 252 810 252 1011 0.4838922
## MKy 156 306 530 195 680 195 836 0.4681517
## MKn 75 123 178 94 226 94 301 0.4657828
## N 28 59 85 33 116 33 144 0.4623861
## err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## PKn 0.4578774 NA 0.4895149
## PKy 0.4499553 NA 0.5005122
## SKy 0.4725785 NA 0.4844434
## SKn 0.4609865 NA 0.4742846
## MKy 0.4419451 NA 0.4546986
## MKn 0.4584762 NA 0.4654458
## N 0.4574473 NA 0.4655529
## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## 888.621814 239.628103 1148.311557 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## 1.000000 1.000000 1.000000 1957.000000
## .n.New.D .n.New.R .n.OOB .n.Trn.D
## 33.000000 589.000000 502.000000 1038.000000
## .n.Trn.R .n.Tst .n.fit .n.new
## 1421.000000 622.000000 1957.000000 622.000000
## .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean
## 2459.000000 3.417574 3.199266 NA
## err.abs.trn.mean
## 3.334452## [1] "Features Importance for selected models:"## All.X.expoTrans.spatialSign.rcv.glmnet.imp
## Q115611.fctrYes 100.000000
## Q113181.fctrNo 95.353402
## Hhold.fctrPKn 72.373641
## Q116881.fctrRight 63.510220
## Q99480.fctrNo 54.770504
## Q98869.fctrNo 53.839298
## Q106997.fctrGr 53.829347
## Q101163.fctrDad 52.946272
## Q98197.fctrNo 51.289537
## Hhold.fctrPKy 48.897373
## Q113181.fctrYes 37.083756
## Q119650.fctrGiving 35.721172
## Q124742.fctrNo 33.972679
## Q115611.fctrNo 33.931389
## Hhold.fctrMKy 32.028548
## Q106272.fctrNo 28.769646
## Q105655.fctrYes 28.149909
## Q114517.fctrNo 26.310633
## Q123621.fctrYes 24.337487
## Hhold.fctrPKy:.clusterid.fctr4 24.070888
## Income.fctr.C 23.655641
## Q100562.fctrNo 21.953249
## Q109367.fctrNo 21.463578
## Q120472.fctrScience 19.045780
## Income.fctr.Q 18.779402
## Q115899.fctrCs 17.701623
## Q118892.fctrNo 16.927606
## Q116881.fctrHappy 16.826284
## Q110740.fctrPC 16.247891
## YOB.Age.fctr(35,40]:YOB.Age.dff 14.929799
## Hhold.fctrSKy 14.586868
## Income.fctr^6 14.352956
## Q108855.fctrUmm... 14.107061
## Hhold.fctrSKy:.clusterid.fctr3 13.949194
## Q104996.fctrYes 13.326780
## Hhold.fctrN:.clusterid.fctr4 13.253891
## Hhold.fctrMKy:.clusterid.fctr4 13.164804
## Q99716.fctrYes 12.607818
## Q116953.fctrNo 12.424004
## Hhold.fctrSKn:.clusterid.fctr2 11.397729
## Q108855.fctrYes! 1.072749
## Hhold.fctrPKn:.clusterid.fctr2 0.000000
## Final.All.X.expoTrans.spatialSign.rcv.glmnet.imp
## Q115611.fctrYes 100.000000
## Q113181.fctrNo 70.459984
## Hhold.fctrPKn 34.259597
## Q116881.fctrRight 56.769317
## Q99480.fctrNo 23.670214
## Q98869.fctrNo 34.890584
## Q106997.fctrGr 24.282362
## Q101163.fctrDad 46.078549
## Q98197.fctrNo 58.192817
## Hhold.fctrPKy 23.473593
## Q113181.fctrYes 35.754276
## Q119650.fctrGiving 0.000000
## Q124742.fctrNo 0.000000
## Q115611.fctrNo 34.294803
## Hhold.fctrMKy 8.755206
## Q106272.fctrNo 15.121509
## Q105655.fctrYes 0.000000
## Q114517.fctrNo 0.000000
## Q123621.fctrYes 0.000000
## Hhold.fctrPKy:.clusterid.fctr4 0.000000
## Income.fctr.C 0.000000
## Q100562.fctrNo 1.705729
## Q109367.fctrNo 0.000000
## Q120472.fctrScience 1.909118
## Income.fctr.Q 8.557590
## Q115899.fctrCs 3.862864
## Q118892.fctrNo 0.000000
## Q116881.fctrHappy 0.000000
## Q110740.fctrPC 23.109030
## YOB.Age.fctr(35,40]:YOB.Age.dff 8.099951
## Hhold.fctrSKy 0.000000
## Income.fctr^6 5.566626
## Q108855.fctrUmm... 0.000000
## Hhold.fctrSKy:.clusterid.fctr3 0.000000
## Q104996.fctrYes 0.000000
## Hhold.fctrN:.clusterid.fctr4 0.000000
## Hhold.fctrMKy:.clusterid.fctr4 20.829954
## Q99716.fctrYes 1.011410
## Q116953.fctrNo 1.975859
## Hhold.fctrSKn:.clusterid.fctr2 0.000000
## Q108855.fctrYes! 14.920312
## Hhold.fctrPKn:.clusterid.fctr2 23.035727## [1] "glbObsNew prediction stats:"##
## D R
## 33 589## label step_major step_minor label_minor bgn end
## 6 predict.data.new 3 0 0 669.282 682.556
## 7 display.session.info 4 0 0 682.556 NA
## elapsed
## 6 13.274
## 7 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn end
## 1 fit.models_1 1 0 0 9.967 535.005
## 2 fit.models 1 1 1 535.006 597.160
## 4 fit.data.training 2 0 0 600.612 658.729
## 6 predict.data.new 3 0 0 669.282 682.556
## 5 fit.data.training 2 1 1 658.730 669.282
## 3 fit.models 1 2 2 597.161 600.612
## elapsed duration
## 1 525.038 525.038
## 2 62.154 62.154
## 4 58.117 58.117
## 6 13.274 13.274
## 5 10.552 10.552
## 3 3.451 3.451
## [1] "Total Elapsed Time: 682.556 secs"
## label step_major step_minor label_minor bgn end
## 4 fit.models_1_preProc 1 3 preProc 13.266 534.995
## 1 fit.models_1_bgn 1 0 setup 13.244 13.253
## 3 fit.models_1_All.X 1 2 glmnet 13.260 13.266
## 2 fit.models_1_All.X 1 1 setup 13.254 13.259
## elapsed duration
## 4 521.729 521.729
## 1 0.009 0.009
## 3 0.006 0.006
## 2 0.005 0.005
## [1] "Total Elapsed Time: 534.995 secs"